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third-world countries. The government gives tax benefits to people and companies who

want to purchase computers. In 1996, the penetration level of telephones (landlines) was

14 telephones for every 100 people, and mobile phones was 2% (Central Intelligence

Agency, 2003). Broadband communication and technologies such as Integrated Services

Digital Network and Asymetric Digital Subscriber Line are also available in Kingston

and most of the major cities around the country. There are presently over 1.5 million

cellular customers, the majority of whom are teenagers and young adults.

The research was conducted in the school of computing at a university in Jamaica

that originated as Jamaica Institute of Technology in 1958 with 50 students and four

programs (University of Technology--Jamaica, 1997). Now, the enrollment is more than

7,500, and more than 300 academic staff serve the following five faculties: Built

Environment, Business and Management, Education and Liberal Studies, Engineering

and Computing, and Health and Applied Sciences. This university offers diploma

programs, undergraduate degree programs, and a few postgraduate degree programs. The

research degrees are at a very minimum level, but the university administration promotes

them. All the academic staff have master’s degrees, but only a few have doctorates. This

university encourages its academic staff to acquire their doctoral degrees and necessary

teaching skills and qualifications. As an example, the university provides funds to

purchase a laptop computer for every lecturer. This university offers many facilities,

including 14 computer labs and other smaller facilities for students’ use. Among other

facilities are a gym, sports center, swimming pool, library, medical center, bookshop,

hostel, and two cafeterias.

The faculty of engineering and computing has two schools: the School of

Engineering, and the School of Computing and Information Technology (SCIT). The



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SCIT offers three types of degree programs in addition to various certificate and diploma

programs. These degree programs are full-time day, full-time afternoon (starting at 3:00

p.m.), and part-time evening (starting at 6:00 p.m.). All of the programs are delivered by

face-to-face instructional activities such as lectures, labs, and tutorials. Most of the

full-time afternoon and part-time evening students are working persons. Many complain

about their unmanageable study work load (according to the program director’s status

report). More than 7% of the students are regional students from other English speaking

Caribbean countries. Attendance varies considerably among these groups. Full-time

student attendance is generally high (approximately 88%), but student attendance of the

afternoon and evening groups is generally low. Some class records show that less than

10% of these students are attending the face-to-face lectures. The students’ grade sheets

indicate that there is a direct relationship between their grades and attendance.

Communication

In this study, the word communication refers to instructional communication in

education. According to Heinich, Molenda, Russell, and Smaldino (1999), instructional

communication may be defined as “the transmission of information from source to a

destination” (p. 12). A number of different visual and mathematical models have been

developed to explain the process of communication. As stated by Heinich et al., in “the

simplified model” (p. 13), there are four components: source, receiver, medium, and

message. The source and receiver are determined by the type of communication (or

interaction). There are three types of communication: learner-content, learner-instructor,

and learner-learner (Moore, 1989). Hilman, Willis, and Gunawardena (1994) added a

fourth type: learner-interface communication. This research studied learner-instructor

communication.



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Communication can be asynchronous or synchronous. In asynchronous

communication, there is a time gap between the two processes of sending and receiving,

whereas in synchronous communication, the processes take place at the same time.

There are several media and methods available for asynchronous communication.

The two most popular methods using electronic media are e-mail and threaded

discussions. Although e-mail communication is widespread, even in third-world

countries, the use of threaded discussions is less common due to its cost of

implementation.

According to Simonson and Thompson (1997), e-mail may be defined as “a type

of software that provides for the easy sending and receiving of messages from one

computer to another” (p. 368). It combines the techniques used in traditional telephones

with conventional mail (post). E-mail is instantaneous, similar to telephone, but one can

send longer, more detailed messages. According to Simonson and Thompson, “Messages

do not interrupt the person they are sent to because they can be stored in the receiver’s

computer, or in a host computer, until they are ready to be read” (p. 232).

There are two types of e-mail systems: Internet service provider (ISP)-based

e-mail and Web-based e-mail. ISP-based e-mail is the original e-mail system that dates to

the late 1970s. Although it was not called ISP-based e-mail at that time, this original

e-mail system was in use even before the Internet was invented. Web-based e-mail

provided by Yahoo, MSN, and AOL is often free of charge; the user must have a

continuous Internet connection throughout the e-mail reading and sending process that

may be more expensive than using ISP-based e-mail. In ISP-based e-mail, the user may

connect to the Internet for only a few seconds to download the e-mails and store them in

the user’s computer.



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Distance education programs use e-mail as a medium for content delivery. In

traditional face-to-face teaching environments, e-mail is being used as a motivational

tool. In the next section, motivation, theories of motivation, and models of motivation are

discussed.

Motivation

According to Schunk (1990), motivation “refers to the process whereby

goal-directed behavior is instigated and sustained” (p. 3). As stated by Weiner (1990),

motivation is also “a work-related rather than a play-related concept” (p. 621). Many

researchers are convinced that motivation plays an important role in students successfully

completing courses. Bohlin (1987) argued that motivation is the backbone of effective

instruction. Keller (1998) said that motivation is one of the main influences on students’

performance.

Learning is a process of accomplishing a task. The task here is gathering, storing,

and retrieving knowledge. For any task to be successfully accomplished, motivation is a

key factor. With growing changes in the educational curriculums and students’ lifestyles,

the reasons for students to continue studies or initiate studies are diminishing. In this

context, instruction that is systematically developed by utilizing motivational theories can

play a major role in the continuation of students’ learning. The next section briefly

discusses the theories of motivation. At the end of the next section, the reason for

selecting the ARCS model in this study is also explained.

Theories of Motivation

Weiner (1992) divided motivational theories into two groups: mechanistic

theories and cognitive theories. The mechanistic theories explain motivation as a result of

needs, drives, and instincts. These theories use models based on drives and instincts and



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do not take into consideration that humans are aware of their own actions. Most of these

models are based upon experiments with animals. The mechanistic theories category

includes two theories: drive theory and field theory.

The cognitive theories are based on the belief that results of information are

acquired. These theories focus on perception and thoughts that are related to action. The

cognitive theories category includes expectancy-value theory, the theory of achievement

motivation, the social learning theory, and the attribution theory. The following

subsection mentions the two groups of motivational theories: mechanistic theories and

cognitive theories.

Mechanistic theories. Hull (1943), an engineer in the field of robotics, formulated

drive theory. This theory was partly derived from the laws of learning. Hull argued that it

was a need, a state of deficiency, not an instinct that caused organisms to initiate certain

behaviors.

Lewin’s (1953) field theory includes some of Hull’s (1943) tension reduction

theories. The principles of the Gestalt psychology (Wertheimer, as cited in L. Visser,

1998) have greater influences to Lewin’s field theory. Gestalt psychology is based on the

concept that comprehension of perceptual phenomena is not possible if observations are

broken down or analyzed in component parts. The whole is often different from the parts

(Weiner, 1992).

Cognitive theories. The expectancy-value theory, developed by Atkinson (1964)

and Rotter (1966), assumed that the kind of behavior that is undertaken depends not only

on the likelihood that the behavior will lead to the desired goal but also on the subjective

value of this goal. The expectancy-value theory is a product of Atkinson’s theory of

achievement motivation and Rotter’s social learning theory. Those two theories are



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discussed next.

The achievement theory developed by Atkinson (1957) elaborates the ideas that

personal differences play an important role. Atkinson’s studies were based on the work of

McClelland, Atkinson, Clark, and Lavell (1953) who carried out studies of personal

motivation related to achievement.

Rotter’s (as cited in L. Visser, 1998) social learning theory is concerned with the

choices people make when they are confronted with a number of alternative behaviors

and outcomes. Rotter argued that people engaged in those actions were expected to bring

the most rewarding goals; thus, expectancy is considered to be a key concept in Rotter’s

theory. Years earlier, Tolman (as cited in L. Visser, 1998) stated that human behavior is

largely determined by the expectancy of a reward and value attached to that reward.

The attribution theory developed by Heider (1958) stated that people ascribe their

success and failures to internal or external causes. L. Visser (1998) stated,

Internally oriented people attribute their failures and successes to their own effort

or ability, or to a combination of two. Externally oriented people attribute their

successes and their failures to factors beyond their control such as a task that was

too difficult or just bad luck, or a task that was too easy or good luck. (p. 49)

Heider’s attribution theory is partly based on the work of Tolman (1932), Rotter (1966,

and Atkinson (1964). Weiner (1985, 1986) also contributed to Heider’s theory.

Models of Motivation

Models derived from one or more motivational theories are useful to ensure a

systematic approach to instructional design (L. Visser, 1998). Three motivational models

are discussed here: Clark’s (1997, 1998) commitment and necessary mental effort

(CANE) model, Wlodkowsky’s (1985) time continuum model (TCM), and Keller’s



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(1984) ARCS model.

CANE model. Clark’s (1998, 1999) studies proposed a motivational model that

defined motivation as having two processes: commitment and necessary effort.

Commitment refers to pursuing a goal actively over time in the face of destruction,

whereas effort is concerned with the amount and quality of nonautomatic elaborations

people invest in achieving the knowledge. In the CANE model, there are three

independent variables affecting commitment: personal agency, mood, and values.

The first factor influencing commitment is personal agency that includes

self-efficacy and contextual factors. Ford (1992) suggested that personal agency involves

two concerns: whether we have the required knowledge to achieve the goal (relating to

self-efficacy) and whether there are barriers to our performance in the work setting

(relating to contextual factors). Thus, capability beliefs have an impact on skills;

contextual beliefs have an impact on responding to the environment. If we believe our

ability and contextual factors will facilitate achievement of the work goal, our

commitment to the goal will increase. Thus, commitment can be supported by increasing

self-efficacy and changing perceptions for the barriers (Clark, 1998). In addition,

self-efficacy is also the key independent variable effort.

The second factor influencing commitment is our mood and emotions. According

to research findings, positive emotions and moods help to achieve goal commitment

(Ford, 1992).

The third factor influencing commitment is our personal task values that include

interest, utility, and importance. If we believe achieving a work goal will increase our

effectiveness, our commitment to the goal will increase. Clark (1999) suggested that task

values might be the most important factor increasing work commitment.



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TCM. The TCM focuses on adult learning. In this model of motivation

(Wlodkowsky, 1985), the learning sequence or process is divided into three periods of

time. For each of these periods, there are two major factors of motivation. These factors

of motivation serve as categories for strategies that can be applied with minimum impact

during those periods of time.

The first period, according to Wlodkowski (1998), called Beginning, is when the

learners enter the learning process. The two subcategories in this time period are Attitude

and Need. The first subcategory, as stated by Wlodkowski (1998), is Attitude: “The

learner’s attitudes toward the general learning environment, instructor, subject matter,

and self” (p. 98). According to Wlodkowski (1998), the second is Need: “The basic needs

within the learner at the time of learning” (p. 98).

The second period, During, is when the learner is involved in the main content of

the learning process. The two subcategories in this time period are Stimulation and

Affect. According to Wlodkowski (1998), the first subcategory is Stimulation: “The

stimulation process affecting the learner via the learning experience” (p. 98). As stated by

Wlodkowski (1998), the second is Affect: “The affective or emotional experience of the

learner while learning” (p. 98).

In the final period, Ending, the learner completes the learning process. The two

subcategories in this time period are Competence and Reinforcement. According to

Wlodkowski (1998), the first subcategory is Competence: “The competence value for the

learner that is a result of the learning behavior” (p. 98). As stated by Wlodkowski (1998),

the second is Reinforcement: “The reinforcement value attached to the learning

experience for the learner” (p. 98).

Although there are six motivational factors in this model, their influences are not



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equal. Each is quite powerful. Therefore, the instructor can organize motivational

strategies for at least one major factor in each time period so that a continuous and

interactive motivational dynamic is organized for maximum effective instruction. If any

of these periods is motivationally vacant, learners may have difficulty sustaining their

motivation for learning (Wlodkowski, 1998). The following six basic questions by

Wlodkowski (1998) will help an instructor plan any learning sequence or study:

1. What can I do to establish a positive learner attitude for this learning sequence?

(Emphasis on beginning activities).

2. How do I best meet the needs of my learners through this learning

sequence? (Emphasis on beginning activities).

3. What about this learning sequence, will it continuously stimulate my

learners? (Emphasis on main activities).

4. How is the affective experience and emotional climate for this learning

sequence positive for the learner? (Emphasis on main activities).

5. How does this learning sequence increase or affirm the learners’ feeling

of competence? (Emphasis on ending activities).

6. What is the reinforcement that this learning sequence provides for my

learners? (Emphasis on ending activities). (p. 100)

A limitation of the TCM is that these six factors are presumed to influence

motivation in a timed sequence (J. Visser, 1990). On the other hand, Keller’s (1984)

ARCS model provides a problem-solving model that enables the instructional designer to

analyze the motivational requirements of the audience. Keller’s (1984) ARCS model will

be discussed next.

Keller’s ARCS model. For the past 2 decades, Keller (as cited in Driscoll, 2000)



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has been developing and testing an integrated model for understanding motivation, and

for systematically incorporating motivational concerns into instruction. Keller (1983,

1984) assumed that students’ motives (or values), together with their expectancies

(efficacy and outcome expectation), will influence the degree of attention and effort they

will supply to a learning task. Keller (1983) proposed four conditions for motivation that

must be met to have a motivated learner: attention, relevance, confidence, and

satisfaction.

According to Keller (1983), gaining and maintaining the student’s attention is

important at the beginning and during instruction. The perceptual arousal can be

increased with the use of novel, surprising, incongruous, and uncertain events. The

inquiry arousal can be increased by stimulating information-seeking behavior and by

posing or having the learner generate questions or a problem to solve. The teacher can

maintain students’ interest by varying the elements of instruction.

The teacher can emphasize relevance within the instruction to increase motivation

by using concrete language and examples with which students are familiar, by providing

examples and concepts that are related to learners' previous experiences and values, by

presenting goal-orienting statements and objectives, and by explaining the utility of

instruction for both present and future usage. The teacher can increase the students’

confidence by enabling them to succeed, by presenting a degree of challenge that allows

for meaningful success under learning and performance conditions, by showing the

student that his or her expended effort directly influences the consequences, by

generating positive expectations, by providing feedback, and by supporting internal

attributions for success.

Providing opportunities to use newly acquired knowledge or skill in a real or



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simulated setting can increase students’ satisfaction. Providing feedback and

reinforcements that will sustain the desired behavior, maintaining consistent standards

and consequences for task accomplishments, managing reinforcement, and keeping

outcomes of learners’ efforts consistent with expectations can also increase the students’

satisfaction.

Keller’s ARCS model is based on Keller’s (1979) theory of motivation,

performance, and instructional influence. It is a macro theory that incorporates cognitive

and environmental variables in relation to effort, performance, and consequence. It has

been used in research on motivational interventions in a face-to-face context (J. Visser,

1990) and in computer-assisted instruction (Song & Keller, 2001).

Wlodkowsky’s (1985) TCM of motivation has many similarities with Keller’s

ARCS model but differs in that the ARCS model has strategy selection from a set of

categories and subcategories based on a synthesis of concepts and theories in human

motivation and, thus, is a flexible model (Visser, Plomp, Amirault, & Kuiper, 2002).

Wlodkowsky’s (1985) model, on the other hand, is a prescriptive solution. The

nonprescriptive nature of the ARCS model has been proven to be practical, widely

applicable, and has additionally withstood the test of cross-cultural application (Keller,

1999).

In summary, Keller’s ARCS model is useful for instructional design because it

provides a systematic approach to the motivational design process; it provides

motivational strategies in instructional design; and it helps instructional designers to

understand the construct of motivation in terms of four distinct categories: attention,

relevance, confidence, and satisfaction.

Among the distinguishing features of the ARCS model is its flexibility, ease of



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use, and systematic approach to the design process of instruction. Keller (1987b)

developed a 12-step design process based on the ARCS model (see Appendix A). The

activities conducted under those 12 steps are divided into four main categories: Define,

Design, Develop, and Pilot.

The Define category includes audience motivation analysis, motivational

objectives, and motivational criterion measures. The Design category includes generate

potential strategies, select strategies, and integrate strategies. The Develop category

includes prepare motivational materials, enhance existing motivational materials, and

developmental test. The Pilot category includes implement with target population,

evaluate effects, and certify or revise.

L. Visser (1998) simplified Keller’s process, reducing it to four steps by

combining pairs of Keller’s steps. The steps of L. Visser’s model are

Step 1. Environmental and audience information

Data collection on the program, course materials, implementation

processes, and instructional processes are performed. Detailed information on the

international students, student social and cultural backgrounds, reasons for taking

the course, and student expectations and expected motivational problems is

collected.

Step 2. Environmental and audience analysis

Based on information obtained in Step 1, an audience analysis is

conducted. The audience analysis consists of a socio/geographic motivational

profile. Analysis of the environment, including problems and deficiencies of the

program/course of the providing institution and of the students’ environment are

also conducted.



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Step 3. Objectives, measures, and preliminary design

Information collected in Step 2 is used to define the motivational

objectives and the content of the communications/messages. Any motivational

tactics that are created are need-based. The initial concept for design of

motivational messages is developed.

Step 4. Final design, development, and testing

Incorporation of enhancing and sustaining tactics in the motivational

communications. Integration into the existing student support system. Formative

evaluation of messages. (p. 64)

Keller’s (1984) ARCS model is selected in this study as the guiding theory. The

reason behind its selection is that it is very useful for instructional design because it has

the following features:

1. It provides systematic approach to the motivational designs process.

2. It provides motivational strategies in instructional design.

3. It helps instructional designers understand the construct of motivation in terms

of four distinct categories: attention, relevance, confidence, and satisfaction.

Statement of the Problem

The main objective of this study was to determine whether there is any difference

in effectiveness between the personalized and the collective process of enhancing

motivation through motivational messages.

Research Question

The following research question was used to guide the data collection and data

analysis: What is the difference, if any, in effectiveness between the personalized and the

collective process of enhancing motivation through motivational messages?



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Significance of the Study

Although numerous studies have been conducted on theories and models of

motivation, little research has been done on applications of those models in different

cultures and environments. In the same way, with the rapid development of technology,

the methods of communication and speed of communication are rapidly changing.

As communication is a method of motivation, there is a greater need of new studies to

verify new communication methods and their role in motivating students.

This study is an application of one of the motivation models, the ARCS model, in

a different cultural background and designing the communication messages and using the

new technology to pass on those messages. Hence, it makes a useful contribution to the

literature.

Summary

It is generally accepted that communication and motivation have a direct

relationship in students’ learning. Properly designed communication can enhance

students’ motivation to learn. The study introduced by this chapter tested this claim.

This chapter defined and described communication, motivation, motivational

theories and models, and the ARCS model, which is the theoretical underpinning of the

study. A statement of the problem, the research question to be examined, and the

significance of the problem were also discussed in this chapter.

In the next chapter, the literature investigating the major concepts of

communication, asynchronous communication, motivation, and motivation models is

reviewed.



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Chapter 2: Review of the Literature

Introduction

This chapter contains six major sections. The first section contains a brief review

of literature related to communication in education, with further review of literature in

e-mail communication, computer-mediated communication, and their applications in

motivation in other parts of the world. The second section discusses motivation and its

historical development in relation to education. The third section, closely related to the

second, examines theories of motivation, mechanistic theories, and cognitive theories and

how these theories evolved and helped in designing motivational models that later

became part of instructional design models. The fourth section further discusses four

different categories of motivational models: person-centered models, environmentally

centered models, interaction-centered models, and omnibus models. The fifth section

discusses obstacles to motivational design and the evolution of the Wlodkowsky (1985)

model and Keller’s (1984) ARCS model. The final section summarizes the chapter.

Communication in Education

In this literature review, the word communication refers to instructional

communication in education (commonly called interaction), defined by Heinich et al.

(1999), as “the transmission of information from source to a destination” (p. 2). Many

visual and mathematical models have been developed to explain the process of

communication. According to Heinich et al., there are four components: source, receiver,

medium, and message. The source and receiver are determined by the type of

communication (or interaction). According to Moore (1989) and L. Visser (1998), there

are three types of instructional communication: learner-content, learner-instructor, and

learner-learner. Hillman et al. (1994) added a fourth component, learner-interface



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interaction, to the literature discussion.

Effects of communication. Hiltz (1994) identified that many students dropped out

of an online course because they simply let the course get away from them. That is, they

put off working on the course until it was too late to finish the course objectives in a

timely manner. Hiltz further stated that students who dropped the course cited reasons

such as inability to schedule their time effectively. Belawati (1998) indicated that one of

the major factors for a lack of active participation is whether the students’ study

approaches and the instructional design of the courses are congruent. Dellana, Collins,

and West (2000) noted that poor attendance in an online and traditional course are shown

to be detrimental to students’ performance. Bourne, McMaster, Rieger, and Campbell

(1997) noted that students tend to procrastinate more in online courses. Miller (2001)

stated,

Therefore, it is a fundamental conclusion that in both traditional and online

courses the learner must be an active participant in order to be successful. The

traditional course has an advantage over the online course as it facilitates active

participation through requirements to meet at a specified time and place. (p. 1)

That is, the traditional course has the structure that traditional and nontraditional students

are familiar with and therefore tends to have more active participation.

E-mail communication. Palloff and Pratt (1999) suggested that a strategy for

successful implementation of asynchronous learning networks include a method of

monitoring student participation and activity levels. Further, Bourne et al. (1997)

suggested that feedback to students is important for them to understand that they are

progressing well on the course. Therefore, according to Miller (2001), “One might

conclude that if a student was informed of their activity and participation levels, he or she



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might respond positively to the feedback and subsequently adjust their participation level

in the course” (p. 1). This proposition was earlier supported by Lee (1994) who suggested

that individuals who “receive an e-mail are not passive recipients of data, but active

producers of meaning” (p. 154). That is, an e-mail message can produce the same

fundamental response as a face-to-face message (Chidambaram & Jones, 1993; Markus,

1994). Therefore, e-mail messages alerting the learner as to his or her current state of

activity and participation should serve to facilitate the need for structure in his or her

course experience (Miller).

Studies on computer-mediated communication also suggested that the type of

e-mail message sent will influence learning activity. Surinder and Cooper (1999) found

that positive messages tend to increase task-oriented activity. McComb (1994) indicated

that computer-mediated communication messages will tend to focus the student on an

instruction’s caring nature.

E-mail communication in educational activities is not limited to the North

American region only. E-mail started to affect Australian schools as early as 1986 when

an evaluation was made of e-mail in a child’s distance education course (Vivian, 1986).

One year later, Beazley (1987) reported on the Australaskan writing project (a

computer-based intercultural exchange program) according to Beazley (1989) and later

noted that reading could be stimulated by having “computer pals across the world” (p.

600). Meanwhile, students are becoming familiar with e-mail through classroom projects

(Boyden, 1988). Walters (1990) recognized the potential for electronic bulletin boards

within a curriculum, whereas others advocated broadening distance education options

through an electronic student lounge (Phillips, 1990). Rehn (1992) envisaged that

technology would overcome the problem of distance in Australia. Butterworth, Keep, and



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Oliver (1993) examined a major e-mail project for western Australian learners. By 1995,

Education Network Australia had arrived, and cyberspace entered classrooms. Education

Network Australia is an initiative, in which governments have agreed to work together to

reduce the cost to users of accessing network services, to explore the joint purchases of

computer equipment for educational institutions, and to encourage the development and

use of Australian education services for access by learners and educational practitioners.

Threaded discussions. Threaded discussion is a further development of e-mail

communication. Threaded discussions are more organized and structured than

conventional e-mail communications. The messages are stored in a central online server

that allows the participants to view or add to the comments. The moderator (usually the

lecturer) of the discussion can add new topics (or threads) to the discussion while

students comment on those topics. As all the discussions are organized automatically, the

students do not lose the track of the discussion. This is unlike e-mail where a user has to

be an e-mail expert to organize each topic, creating new mailboxes (or folders and

subfolders). Although threaded discussions are usually asynchronous, with new

technology, it can also be synchronous. The drawback of threaded discussion is that it

needs expensive infrastructure (in hardware and software) compared to e-mail

infrastructure. Most Third World countries still cannot afford those costs; hence, the

penetration of these threaded discussions in those countries is at a very low level. The

University of Technology where the writer is based still does not have the facilities for

this type of discussion (Wickramanayake, 2002).

Threaded discussions have been commonly used by American distance education

institutions such as University of Phoenix and Nova Southeastern University since the

late 1990s. Although the infrastructure is widely available, lack of technical knowledge



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by the lecturers or the moderators still keeps this technology at a low level of penetration,

even in the North American region. Caswell (2001) stated, “Thus, it appears that use of a

threaded discussion forum is an appropriate meaningful integration of technology and

education, and I continue to use it in my class today” (p. 4). In 2001, at Oswego High

School in New York, Caswell used threaded discussions to teach social studies at

secondary level. According to Caswell, the findings of the research conclude that

“although the participation in the forum was purely optional, nearly half of the students

surveyed actively used the forum” (p. 4).

Short Message Service (SMS). SMS refers to the transmission of short text

messages to and from a mobile phone. Messages must be no longer than 160

alphanumeric characters and contain no images or graphics. According to the Webopedia

Dictionary (n.d.) Web site,

Once a message is sent, it is received by a Short Message Service Center (SMSC),

which must then get it to the appropriate mobile device. To do this, the SMSC

sends a SMS Request to the home location register (HLR) to find the roaming

customer. Once the HLR receives the request, it will respond to the SMSC with

the subscriber's status: 1) inactive or active 2) where subscriber is roaming. If the

response is "inactive", then the SMSC will hold onto the message for a period of

time. When the subscriber accesses his device, the HLR sends a SMS Notification

to the SMSC, and the SMSC will attempt delivery. The SMSC transfers the

message in a Short Message Delivery Point to Point format to the serving system.

The system pages the device, and if it responds, the message gets delivered. The

SMSC receives verification that the message was received by the end user, then

categorizes the message as “sent” and will not attempt to send again. (para. 1)



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According to the GSM Association Web site, about 30 billion SMS messages are

sent globally every month (GSM Association, n.d.). These have primarily been used as a

means of communication among people. SMS messages are being used in almost all the

aspects of human life, but usage of SMS in education and motivating students is

worthwhile mentioning.

TXT bites from General Certificate of Secondary Education (GCSE) bitesize.

GCSE students in Britain can sign up to receive top revision tips via their mobile phones

thanks to TXT bites, a new feature from GCSE bitesize, masterminded by BBCi Schools.

Commenting on TXT bites and SMS’s ability of motivating students, M. Suri (as quoted

by the British Broadcasting Corporation), editor for BBCi Schools, said,

Text messaging is one of the most popular methods of communication for people

in the GCSE age group, so what better way to encourage them to revise for their

exams than using text messages to give them some valuable exam tips. (para. 1)

TXT bites delivers 24 sets of GCSE questions and answers in a choice of five key

subjects to callers who register via a dedicated telephone line that is provided by the

British Broadcasting Corporation Audiocall. Open until 30 April every year, the text

messages cover mathematics, English, biology, chemistry, and physics. Users received 48

messages per subject between the period from February 18, 2002 to May 16, 2002.

The questions are texted first with the answers following once recipients have had

a chance to think about them. TXT bites was promoted on-line (see Web address below)

as well as on post cards sent to schools. Recipients can reply to text messages if they are

having any revision problems, and the 10 best questions will be posted on the SOS

teacher Web site (www.bbc.co.uk/sosteacher), alongside an answer from an experienced

teacher.



22

The British Broadcasting Corporation has recently introduced paid SMS text

messages called TXT bites for GSCE revision. Students can use SMS to receive exam

results, to be encouraged and motivated by a friendly message from the teacher, or to

send a SMS message in class to pay attention to avoid embarrassment in front of others.

SMS can also be used to provide answers to multiple-choice questions, draft an agenda

on the move, negotiate simple contracts and design promotional messages. Students can

also participate in class sessions via text chat. Testing the limits of SMS for blended

learning causes experimentation and invention.

SMS tutorial. A tutorial was given to a cohort of 197 students from the School of

Business and Accountancy at Ngee Ann Polytechnic in Singapore (Randall, Seet, Lim, &

Elangovan, n.d.). They were required to use SMS to carry out the following tasks:

1. Design an SMS advertisement to promote a business service or product.

2. Negotiate a contract to sell some goods with their tutor using SMS.

3. Purchase a can of soft drink using SMS and send a SMS message to their tutor

concerning the steps taken to obtain the drink.

4. Create an agenda for a meeting a business is having the next day and SMS it to

their tutor as if he was a member of the company.

Approximately 75% of participants agreed that the tutorial enabled them to see

the potential of SMS as an e-learning tool. The survey results confirmed the popularity of

the mobile phone as a means of communication among students with 90% agreeing that

they used their mobile phones more than 10 times per week. The objectives of the tutorial

were met as 85% agreed that before the tutorial, they did not realize that a mobile phone

could be used for tutorials, and 75% agreed that the tutorial enabled them to see the

potential of SMS as an e-learning tool. Sixty percent agreed that they would like more



23

tutorials to be conducted through a mobile phone.

The other learning objective of making students aware of the use of SMS as a tool

for conducting business was also accomplished, as 70% agreed that the tutorial made

them realize that SMS can be used to make contracts. Overall, the SMS tutorial was

positively received by the students as the majority of them found that the topics in the

tutorial were interesting (75%), that they could complete the tutorial easily through their

mobile phones (80%), and that the tutorial was pegged at a reasonable level of difficulty

(80%).

The mobile phone has also been used in the lecture by presenting students with a

legal problem then requiring them to answer the question posed, give a reason, state the

name of the relevant case, and SMS the answer to the lecturer in real time. Speed and

accuracy are taken into consideration.

Motivation

According to Schunk (1990), “Motivation refer[s] to the process whereby

goal-directed behavior is instigated and sustained” (p. 3). As stated by Weiner (1990),

motivation is also “a work-related rather than a play-related concept” (p. 621). Weiner

(1990) also stated, “Motivation is often inferred from learning, and learning is an

indicator of motivation for the education psychologist” (p. 618). If this is the case, how

do we motivate students to engage in learning?

Another question of equal importance is, How do we help learners develop

self-regulatory skills to set their own goals and manage their own learning and

performance? Schunk and Zimmerman (1994) defined self-regulation as “the process

whereby students activate and sustain cognitions, behaviors and effects, which are

systematically oriented towards the attainment of their goals” (p. 389). Weiner (1990)



24

wrote, “At one time, motivation was the dominant field of study [in psychology]” (p.

616). This was primarily true because psychologists such as Tolman and Hall (as quoted

by Weiner, 1990) in the late 1930s and 1940s conceived motivation as “what moved a

resting organization to a state of activity” (p. 617). Hall (as cited in Weiner, 1990), for

example, developed a theory of learning in which behavior is presumed to come about as

a result of drives toward anticipated goals. That is, behavior is motivated toward a goal

by the existence of some (usually biological) need; for example, a need for food, sex, or

shelter.

Tolman’s (as cited in Weiner, 1990) research on latent learning, however, had the

effect of separating concern about motivation from concern about learning. Tolman (as

cited in Wiener, 1990) demonstrated that animals appeared to learn a maze simply by

exploring it, in the absence of a goal or incentives for drive reduction.

In the 1960s and 1970s, American psychology shifted from a behavioral to a

cognitive perspective that brought a reintegrating of motivation with learning.

Researchers began to examine in new ways the effects of rewards on behavior. According

to new findings, rewards have little effect on subsequent behavior unless learners

generate expectancy for, or an anticipation of, the reward (Estes, 1972) although it had

been widely accepted that rewarding a response automatically increased the likelihood of

its recurrence. Deci (1975) found that some rewards, if perceived by the learners as

controlling, tend to reduce their natural interest in the learning task. Similarly, rewards

for the completion of an easy task tend to signal to learners that they are low in ability.

Hence, reward can mean a variety of different things, and each meaning can have

different motivational and learning consequences.

With the new shifts in research, White (1959) argued that achievement





26

learning. Anxiety, just like need, was considered an aversive stimulus. Spence, Taylor,

and Ketchel (1956) argued that there is an interaction between the drive level and how

easy or complex tasks are performed. Even though the drive theory approach was very

methodical and almost mechanical, it lacked cognitive processes and adhered to an

almost robotic view of humans (Weiner, 1992). Filling the gap, Lewin’s (1935) field

theory incorporated some of Hull’s (1943) tension reduction theories, but these are

embedded in a more cognitive notion of behavior. Gestalt psychology, which is the base

for the Lewin’s theory, states that comprehension of perceptual phenomena is not

possible if observations are broken down or analyzed in component parts. Most

significant is the whole (or total) situation perceived by the person.

Lewin (1935) and Hull (1943) were convinced that the goal of motivational

theory is to identify the determinants of behavior. But, according to L. Visser (1998),

Field theorists however, are almost exclusively concerned with human behavior

as opposed to the behavior of nonhuman organizations, which until then had

been dominant. They are interested in developing the broader goals of a theory of

motivation and want to know what determines motivated behavior: needs of a

person (drive/tension); and properties of the goal object (incentives). (p. 46)

In summary, the mechanistic motivational theories that have been discussed so far

all have in common some kind of intrapersonal dynamics such as drive, tension, or need.

Although, according to Weiner (1992), these mechanistic theories are still found in the

contemporary study of human behavior, they have a common deficiency in that they have

not been able to give adequate, clear, and measurable observations of events.

Achievement theory, suggested by McClelland et al. (1953), fills this deficiency.

Cognitive theories. Atkinson (1964) and Rotter (1966), in the 1960s and 1970s,



27

argued that the kind of behavior that is undertaken depends on the likelihood that the

behavior will lead to a desired goal and also on the subjective value of this goal. This was

the basic assumption of the expectancy-value theory, which is a cognitive theory.

According to Schunk (1989), people are “motivated to act when they believe that

action will lead to positive outcomes and when they value these outcomes” (p. 16). The

studies conducted by McClelland et al. (1953) on personal motivation-related

achievement showed that some people demonstrated a high need to achieve, to be

successful, and to excel, whereas others showed a tendency to be afraid of failing or had

an indifference toward excelling and, consequently, showed a low need to achieve.

Atkinson (1957), in agreement with McClelland et al. (1953), said that personal

differences that play an important role are, in fact, considered to be the motivating factors

of human motivation. In Atkinson’s (1957) theory of achievement, persons considered to

be high in achievement motivation most likely display different risk-taking behavior from

persons who are considered to be low in achievement motivation. Research indicated that

those who scored a high need for achievement tended to be high achievers and moderate

risk takers, whereas the low need achievers, on the other hand, often tried to do tasks that

were either too difficult or too easy (L. Visser, 1998).

Rotter (as cited in L. Visser, 1998) studied about the choices people make when

they are confronted with a number of alternative behaviors and outcomes. Rotter (as cited

in L. Visser) argued that people engage in those actions expected to bring the most

rewarding goal; this expectancy is considered to be the key in Rotter’s theory. Twenty

years earlier, Tolman (as cited in L. Visser) expressed the same idea that the behavior of

human beings is not only based on stimuli and related needs and drives but is largely

determined by the expectancy of a reward and the value attached to that reward.



28

Heider (1958), Kelley (1967), and Weiner (1985, 1986) argued that people,

especially in the field of achievement, ascribe their successes and failures to internal and

external causes. Thus, although internally oriented people attribute their failures and

successes to their own effort or ability, or a combination of the two, externally oriented

people attribute their successes and failures to factors beyond their control such as a task

that was too difficult or just bad luck (L. Visser, 1998).

Motivational Models

According to Keller (1987a),

It is true that motivation is a challenge, and from one point of view, you can’t

really motivate anybody. You know the old saying; “you can lead a horse to the

water but you can’t make him drink.” However, it is possible to create conditions

that stimulate people’s desire to be interested and involved in their surroundings

and to achieve their best. Many trainers, teachers, and other leaders do it

regularly. (p. 1)

Keller (1979) stated,

We know, as a rule of thumb, that we should introduce novelty, uncertainty, or a

sense of mystery at the beginning of a program to elicit attention and, it is hoped,

enthusiasm, and we know that we should use reinforcement to help sustain

desirable changes in behavior, but neither of these principles constitute an

adequate undertaking of motivation. (p. 26)

Adequate, systematic attention has not been given to the problem of motivation in

instructional theory and technology, to the understanding of motivation in individual

learners, or to the development of a technology for influencing motivation (Cooley &

Lohnes, 1976; Cronbach & Snow, 1976). Motivational design is an aspect of instructional



29

design that refers specifically to strategies, principles, and processes for making

instruction appealing.

Keller (1984) discussed four categories of motivational design models under which

all the following motivational models can be categorized:

1. Person-centered models.

2. Environmentally-centered models.

3. Interaction-centered models.

4. Omnibus models.

The first three models are grounded in psychological theories of human behavior.

The last, Omnibus models, has a more pragmatic origin and incorporates instructional

design and motivational design strategies. Keller (1984) stated, “These models tend to

grow out of solutions to specific kinds of instructional problems” (p. 2).

Person-centered models. These models postulate that people have innate drives,

potentials, values, and motives that influence personal motivation and development

(Keller, 1984). They assume that the primary impetus for psychological growth and

development comes from within the individual. Psychological theories in this area

include research on curiosity and sensation seeking (Berlyne, 1965; Zuckerman, 1971)

and the succession of people such as Maslow, Herzberg, and Rogers (as cited in Weiner,

1986) who are concerned with human motives and self-fulfillment.

Environmentally-centered models. These models assume that behavior can be

adequately explained in terms of environmental influences, that the reinforcement

contingencies in the environment exert powerful controls on human volition (Keller,

1984). Many people are associated with this school of thought, but one of the most

important is Skinner (1968) who applied his concepts to education. The primary result of



30

Skinner’s work became known as programmed instruction, which is a combination of the

instructional design and motivational design models. Skinner’s (as cited in Markle, 1969)

model was the motivational principle of positive reinforcement following correct

responses, and it requires that instruction be structured to ensure correct responses to the

fullest extent possible

Keller (1968) expanded Skinner’s (1968) instructional design model into a plan

for teaching whole courses of instruction called the personification system of instruction

or the Keller plan. It has many of the features used in programmed instruction, and it is a

complete instruction management system grounded in a set of motivational design

strategies based on conditioning principles.

In contrast to the Keller plan, Sloane and Jackson (1974) provided a model that

describes how basic concepts of conditioning and reinforcement can be used to control

the motivation of the students. However, as Deci (1975) and Lepper and Greene (1978)

have shown, intrinsic motivation can be decreased by extrinsic reward systems and can

be very difficult to establish initially or reestablish after being diminished.

Interaction-centered models. Interaction-centered models assume that neither the

personal nor the environmental assumptions provide an adequate basis for understanding

or explaining human motivation. In this approach, human values and innate abilities are

seen to influence and to be influenced by environmental circumstances. These models are

based on social learning theory and expectancy-value theory (Keller, 1983b). DeCharms

and Muir (1978) and Hunt and Sullivan (1974) offered theories and reviews of

motivational research that focus on the interactions of individual traits with

environmental influences on behavior. Brophy (1981) argued that environmental

influences could include social factors such as teaching style and the manner of using



31

praise. DeCharms (1968), working within the general context of expectancy-value theory,

developed an applied model with two major variables: achievement motivation,

representing the value component and personal causation representing the expectancy

component. DeCharms’ model was patterned after the basic work of McClelland (1965)

and Alschuler (1975), but by including the concept of personal causation, it becomes an

interactive model (Keller, 1983b).

Wlodkowsky model. Wlodkowsky (1985) provided one of the first comprehensive,

applied approaches to motivation. Wlodkowsky’s (1985) model includes a large number

of motivational factors, including humanistic and behavioral principles, and it divides

motivational strategies into six categories: attitudes, needs, stimulation, effect,

competence, and reinforcement. According to Keller (1984), this model is called a

“process model” (p. 5) that specifies things to do at the beginning, during, and at the end

of a lesson or module of instruction.

ARCS model. For the past 2 decades, Keller has been developing and testing an

integrated model for understanding motivation and for systematically incorporating

motivational concerns into instruction (Driscoll, 2000). Keller (1983, 1984) assumed that

students’ motives (or values), together with their expectancies (efficacy and outcome

expectation), will influence the degree of attention and effort they will apply to a learning

task. Keller (1983) proposed four conditions for motivation that must be met to have a

motivated learner: attention, relevance, confidence, and satisfaction. According to Keller (1983), gaining and maintaining the student’s attention is

important at the beginning and during instruction. The perceptual arousal can be

increased with the use of novel, surprising, incongruous, and uncertain events. The

inquiry arousal can be increased by stimulating information-seeking behavior or by



32

posing or having the learner generate questions or a problem to solve. The teacher can

maintain students’ interest by varying the elements of instruction.

The teacher can emphasize relevance within the instruction to increase motivation

by using concrete language and examples with which students are familiar, by providing

examples and concepts that are related to learners' previous experiences and values, by

presenting goal-orienting statements and objectives, and by explaining the utility of

instruction for present and future uses.

The teacher can increase the students’ confidence by enabling them to succeed, by

presenting a degree of challenge that allows for meaningful success under learning and

performance conditions, by showing the student that his or her expended effort directly

influences the consequences; by generating positive expectations, by providing feedback,

and by supporting internal attributions for success.

Providing opportunities to use newly acquired knowledge or skill in a real or

simulated setting can increase students’ satisfaction. Providing feedback and

reinforcements that will sustain the desired behavior, maintaining consistent standards

and consequences for task accomplishments, managing reinforcement, and keeping

outcomes of learner's efforts consistent with expectations can also increase the students’

satisfaction.

Keller’s ARCS model is based on Keller’s (1979) theory of motivation,

performance, and instructional influence. It is a macro theory that incorporates cognitive

and environmental variables in relation to effort, performance, and consequence. It has

been used in research on motivational interventions in a face-to-face context (Visser,

1990) and in computer-assisted instruction (Song & Keller, 2001).

Wlodkowsky’s (1985) TCM of motivation has many similarities with Keller’s



33

ARCS (1984) model but differs in that the ARCS model has strategy selection from a set

of categories and subcategories based on a synthesis of concepts and theories in human

motivation and thus is a flexible model (Visser et al., 2002). Wlodkowsky’s (1985)

model, on the other hand, is a prescriptive solution.

The nonprescriptive nature of the ARCS model has been proven to be practical

and widely applicable and has additionally withstood the test of cross-cultural application

(Keller, 1999). In summary, Keller’s ARCS (1984) model is useful for instructional

design because it provides the systematical approach to the motivational design process;

it provides motivational strategies in instructional design; and it helps the instructional

designers to understand the construct of motivation in terms of four distinct categories:

attention, relevance, confidence, and satisfaction.

Keller’s (1984) ARCS model was selected in this study as the guiding theory. The

reason behind its selection was that it is very useful for instructional design because it has

the following three features:

1. It provides the systematical approach to the motivational designs process.

2. It provides motivational strategies in instructional design.

3. It helps the instructional designers to understand the construct of motivation in

terms of four distinct categories: attention, relevance, confidence and satisfaction.

Omnibus models. Omnibus models are best described as complete solutions to

given instructional goals. They are not motivational design models but are included here

because they offer excellent examples of motivational strategies. According to Keller

(1984), these “models sometimes have a theoretical underpinning, but their primary basis

is pragmatic, in that they incorporate a complete system of teaching and instruction

management that is designed to accomplish a specific type of instructional purpose” (p.



34

6). Joyce and Weil (1972, and in three subsequent books) provided the best compilation

of these models, including detailed examples of the models in use and instructions for

learning to use them.

Obstacles to Motivational Design

According to Keller (1984), there are many inherent factors that greatly contribute

to the obstacles to motivational design. There are two factors of great concern. The first is

the unstable nature of the motivation and is related to the difficulties in establishing a

useful theory of motivation. Motivation includes many factors that range from highly

transitory states of arousal to reasonably enduring preferences for given types of

activities. Furthermore, the intensity of these factors can vary tremendously over short

periods of time, and they generally have a nonlinear (inverted u-curve) relationship with

performance (Keller, 1984).

Another side of this problem is the multiplicity of motives and goal orientation of

individuals. At one stage, people tend to have fairly stable orientation and motive

profiles. That is, a person with a high need for achievement will tend to prefer,

predictably, different kinds of activities from a person high on the need for affiliation.

Both these motives can be overridden by another motive, such as a need for physical

security that assumes a higher priority in a given situation. This variability is a challenge

for anyone who tries to develop models of motivational design.

The second problem, which is that of measurement, is closely related to the

preceding. Just as it is difficult to obtain a functional theory of motivation, it is difficult to

measure the important elements of influence and change in motivational design.

According to Keller (1984), there are four sets of variables that have to be

considered in motivational design. First are the characteristics that pertain to motivation,



35

second are the design strategies intended to influence motivation, third are the social and

environmental conditions that might influence the effectiveness of motivational

strategies, and fourth are consequences that present special problems. Keller (1984)

stated,

All too often, changes in achievement are used as the dependent measure for

motivational studies. It is better to use measures of effort, such as time-on-task,

intensity of effort, or latency of response, because these are direct measures of

motivation. Achievement is an indirect measure that is influenced by many

motivational factors, such as ability, prior knowledge, and instructional design

factors. (p. 7)

The four variables discussed above set the theoretical base for Keller’s (1984) ARCS

model.

Summary

The question of what underlies motivation and how teachers can effectively

motivate their students has been the subject of investigation for many years. Although the

theories that have emerged from this research cannot strictly be called learning theories,

the study of motivation for educators is certainly confounded with the study of learning

(Driscoll, 2000).

The definition and application of motivation is evolving with time. According to

Weiner (1980), the psychologists in the 1930s and 1940s conceived motivation as “what

moves a resting organization to a state of activity” (p. 617) in a behavioral approach. In

the 1960s and 1970s, the shift from a behavioral to a cognitive perspective in American

psychology brought a reintegration of motivation with learning. With researchers now

concentrating on human behavior, motivational research is dominated by investigations



36

into humans’ need for achievement (Weiner, 1990). For the last century, many models

were evolved from the many theories of motivation in existence. More significant are two

models: Wlodkowsky’s (1985) TCM and Keller’s (1984) ARCS model. Wlodkowsky’s

(1985) model has many similarities with Keller’s (1984) model but differs in that the

ARCS model has strategy selections from a set of categories and subcategories based on

a syntheses of concepts and theories in human motivation and, thus, is a flexible model

(L. Visser, 1998).

Communication enhances learning and, hence, motivates the student to achieve

goals (Keller, 1984). There are many communication methods in distance and traditional

learning environments, but with the existence of new technology, more efficient

communication methods can attain and sustain the students’ attention (Keller, 1984).

Ferrier (1998), Miller (2001), and Yowell (1995) suggested that e-mail communication is

cheaper and quicker to use in educational environments for communication purposes.

This chapter has reviewed the literature of communication in education,

motivation in education, theories of motivation, models of motivation including Keller’s

(1984) ARCS model, and obstacles to motivational design. The following chapter

describes the methodology used in the study.



37

Chapter 3: Methodology

Introduction

This chapter contains six sections. First, students who were the study’s subjects

are described. The second section describes the manner in which this study was carried

out. The third section describes the instruments that were used in the study, emphasizing

development of the motivational messages system (MMS) according to Keller’s (1984)

ARCS model. The fourth section lists the research constructs, including null hypothesis

and variables. The fifth section addresses data analysis, including scoring of the

instruments and estimates of their validity. The final section summarizes all the above.

Participants

Subjects of this study were the students from the School of Computing and

Information Technology at the faculty of engineering at a university in Jamaica. Two

groups (classes) were selected from the SCIT for the study. The groups were selected

from the part-time students who were doing their bachelor degrees and were in the final

year of their 4-year program. Participants were taught by a single teacher who was also

the researcher. This was to achieve a more controllable environment so that the treatment

was consistent. This also increased the treatment fidelity (Gall, Borg, & Gall, 1996).

Subjects included 8 male and 35 female students. Students consisted mainly of

Jamaican students, ranging from Black, to multiracial, to Indian students. The average

ethnic ratio of Black: Indian in a classroom was 20:1. The age group of students was 18-

30 years. The participants were of different social backgrounds from middle to upper

class, the majority being middle class. Some students were working full-time or

part-time. The evening full-time and part-time students were occupied with their full-time

or part-time jobs. Each group (class) consisted of an average 15-20 of students.



38

Procedure

The personal group was sent personalized motivational messages and the

nonpersonal group received group motivational messages. Only one course was used to

reduce the confounding variables in the research.

The research was conducted during the tutorial sessions. Motivational messages

were communicated using e-mail and SMS. The method of communication (whether

e-mail or SMS) depended on the size (SMS has a limitation of 160 characters per

message), type, timing of the message, and to which motivational aspects of the ARCS

model the message belongs.

The last message, Message 11, was a good example to explain why the timing

was important in deciding the communication method. The impact of seeing the message

on a student’s cellular phone just a few minutes before the final exam cannot be

compared with seeing the same message a day before on their computer screen or seeing

it when the student came home after the exam.

SMS messages were sent to cellular phones from a computer that was configured

to keep a record of all incoming and outgoing messages to and from participants’ e-mail

addresses and SMS (from mobile phones). E-mails were sent to participants’ e-mail

addresses and were viewed on their computers.

The schedule of messages is shown in Table 1. The complete set of messages is

shown in Appendix B.

Instruments

The research design consisted of a treatment and a posttest. However, the

treatment had two different “flavors”: personalized, group motivational messages

intended for two groups. The posttest was to measure the students’ achievement.



39

Table 1

Schedule of Messages Indicating Timing, Reason, and the Method of Sending

____________________________________________________________________________

Message no. When sent Why sent How sent

____________________________________________________________________________

1 Before all Welcoming the students while E-mailcourse work indicating methods of

communications & importance

of them

2 Before all Advice on how to manage the SMS

course work course effectively

3 Before Test 1 Reminding about Test 1 SMS

4 Before Test 1 To encourage students to study for SMS &Test 1 & indicating what to study e-mail

5 Before the Motivate for the assignment SMS

assignment and future tests

6 Before the Motivate for the assignment SMS

assignment

7 Before Motivate for Test 2 SMS

presentation

(Test 2)

8 After midsemester Motivate students, reminding SMS &them how far they have e-mailreached in their studies,

Invite them to give feedback

9 3 weeks before Encourage students that they E-mail

final exam reached the end of the course;

they now have the opportunityto do their best on the exam

10 2 weeks before Offer help for final exam SMS

final exam

11 5 minutes before Wish good luck on final exam SMSfinal exam

________________________________________________________________

Note. SMS = Short Message Service.

The final exam was utilized as the posttest. The final exam was a 2-hour test

consisting 65 of multiple-choice questions and the choice of two essay questions. The



40

scores for the multiple-choice questions were considered as only posttest scores to reduce

the subjectivity of the test scores. The multiple-choice questions were drawn from a

databank. Hence, these questions had being used several times by other teachers who

taught this course. This enhanced the reliability of the posttest. Although some questions

had being used before in previous test papers, this final exam paper (posttest), as a whole

paper, had never been used before.

The student grades (after the treatment) were obtained from the lecturers’ grades

sheets. The e-mail and SMS communications between the researcher and students,

including the students’ feedback for the motivational messages, were recorded. That

feedback was utilized to design the motivational messages that were yet to be sent.

Development of motivational messages. The motivational messages were

developed using the ARCS model. L. Visser’s (1998) and J. Visser’s (1990) research

findings had a great influence on designing the motivational messages in this research. L.

Visser’s study was conducted on distance learning environment, whereas J. Visser’s

study was carried out in a face-to-face learning environment. The simplified version of

the designing of motivational message (discussed in chapter 1) was used (Suzuki &

Keller, 1996; L. Visser, 1998).

The ARCS model (Keller, 1983) is practical for instructional design as it helps to

understand the construct of motivation in four discrete categories: attention, relevance,

confidence, and satisfaction). The model provides a methodical motivational design

process as well as the chance to implement a number of motivational strategies and

tactics.

The ARCS model has proven to be practical and widely applicable and has

additionally withstood the test of cross-cultural applications (Keller, 1999). The ARCS



41

model has certain similarities with Wlodkowsky’s (1985) TCM of motivation but differs

in that the ARCS model has strategy selection from a set of categories and subcategories

based on a synthesis of concepts and theories in human motivation and Wlodkowsky’s

(1985) model is flexible, although it is a prescriptive solution.

Continuing formative evaluation of students’ feedback (to the MMS), entry level

motivation, environment, and the situations were needed to improve the motivational

messages while they were being developed in order to make them valid, realistic, and

effective (L. Visser, 1998). The schedule of messages is shown in Table 1.

The researcher’s past teaching experience was used to analyze the audience and

determine students’ initial (entry) motivational level. The following student description is

given to facilitate the discussion of the audience:

1. Program. Bachelor of Science degree in Computing and Information

Technology given via face-to-face teaching.

2. Students. Students who on average have high school diploma and Caribbean

Examination Council (minimum 5 subjects including mathematics and English).

3. Registration. Once a year.

4. Duration of the course. One semester (15 weeks).

5. Assessment. Tutor-marked case presentation, a class test, and a final exam.

6. Tutoring. Two hours of face-to-face lecture and 2 hours of tutorials per week.

7. Completion rate. 65% (5 year average).

Personalizing motivational messages. The first motivational message was the

only one that was predesigned. All the other messages were shaped by the feedback of

the students for the messages already sent. The group messages were converted into

personalized messages. To personalize a group message; for example, the first name was



42

used to address instead of a common address at the begging of the message (i.e., the

group SMS message, “Dear student, Good luck in your final exam!!! DKW” can be

personalized as, “Peter, Good luck in your final exam!!! DKW.” Some messages were

sent to the personalized group without being personalized. Each personalized message

was sent individually, whereas group messages were sent to an e-mail list or SMS list as

a single message. The mailing lists (e-mail and SMS) were filtered to verify the accuracy

of the list before sending the first message. Any bouncing e-mails or SMSs were resent

the same day.

Research Construct

This study was an experimental research design. A hypothesis was addressed by

the research. A hypothesis was used as a map to gain a clear view and flow of the

direction the study was taking.

Hypothesis. The effectiveness of the personalized messages is greater than that of

the group messages in motivating students.

Variables. There was one dependent variable and one independent variable. The

dependent variable is the achievement that was measured using the posttest. The

independent variable was the type of treatment; that is, a two-level manipulated variable.

Data Analysis and Validity

The instrument used for measuring the achievement (dependent variable) was the

final exam (posttest) of the course. The final exam was set according to the course outline

(syllabus) that clearly defined the behavioral objectives of the course. The course outline

was distributed to the students and explained during the first lecture, making them fully

aware of what was to be examined in the final exam. Hence, the measurements of the

grades from this exam should have been a true reflection of the students’ achievement.



43

Hence, in this sense the instrument was valid.

Summary

The chapter described the methodology of a study to determine whether

personalized MMS was effective than the group MMS. Oftentimes, due to inadequate

attention to their motivation needs, students fall behind. The subject of their study and

their environment were described. The research constructs and the items created to

measure them were listed. Finally, data supporting the validity of the instrument used in

the study were presented.



44

Chapter 4: Results

Introduction

This chapter has four main sections. The first section is the introduction .The

second section presents the test results of the five different types of grades of the

participants of whom one group received personalized motivational messages (PMM),

and the other group received group motivational messages (GMM). The third section

presents the results of additional two tests. The chapter concludes with a summary.

Test Results

This study investigated whether PMM are more effective than the GMM in

motivating students. The directional hypothesis was, The effectiveness of personalized

messages is greater than that of group messages in motivating students.

A total of 44 students participated in the study, but only 43 completed all the tests.

One participant did not sit for the final exam and was removed from the data. Because the

dropout was only 1 participant out of 44, the effects of dropout on the final results were

negligible. Hence, the further analysis of dropouts versus participants was omitted.

Test 1--Midsemester multiple-choice test grades. Table 2 shows the mean values

and standard deviations for the midsemester test (MCQ) results. Because the hypothesis

was directional, a one-tailed t test was conducted. According to the results, although

mean values for the PMM (60.45) were higher than that of GMM (55.14), there was no

significant difference between the groups. Hence, the hypothesis was rejected.

Test 2--Case presentation grades. Table 3 shows the mean values and standard

deviations for the case presentation grades. According to the results, although mean

values for the PMM ( M = 77.61) were higher than that of GMM ( M = 74.40), there was

no significant difference between the groups. Hence, the hypothesis was rejected.



45

Table 2

Means and Standard Deviations for Midsemester Test Grades of Personalized

Motivational Messages Group and Group Motivational Messages Group

___________________________________________________________________________

Group N M SD P(one-tailed)

___________________________________________________________________________

Personalized motivational messages 22 60.45 13.00 0.09

Group motivational messages 21 55.14 12.53 0.09

_______________________________________________________________

Note. p > 0.05.

Test 3--Final exam multiple-choice grades. Table 4 shows the mean values and

standard deviations for the final exam: MCQ grades. According to the results, mean

values for the PMM ( M = 38.27) were higher than that of GMM ( M = 34.71), and there

was a significant difference between the groups ( p < 0.05). Hence, the hypothesis was

accepted.

Test 4--Final exam essay grades. Appendix C shows the mean values and

standard deviations for the final exam essay grades. According to the results, although

mean values for the PMM ( M = 18.82) were higher than that of GMM ( M = 16.19), there

was no significant difference between the groups ( p > 0.05). Hence, the hypothesis was

rejected.

Test 5--Total grades. Appendix D shows the mean values and standard deviations

for the total grades. According to the results, although mean values for the PMM ( M =

65.15) were higher than that of GMM ( M = 62.40), there was no significant difference

between the groups ( p > 0.05). Hence, the hypothesis was rejected (total grade = 20% ×

Test1 + 20% × case presentation + 10% × attendance + 50% × final exam).



46

Table 3

Means and Standard Deviations for Case Presentation Grades of Personalized


___________________________________________________________________________


___________________________________________________________________________



_______________________________________________________________

Note. p > 0.05.

Additional Test Results

Two additional t tests were conducted for two more sets of data. Those were the

test grades for students’ attendance and the total final exam grade (= [Final-MCQ +

Final-Essay] × 100/90). Test 6 was conducted because final MCQ grades showed that the

hypothesis was accepted, whereas the final essay grades showed that the hypothesis was

rejected.

Test 6--Attendance grades. Appendix E shows the mean values and standard

deviations for the attendance grades. According to the results, mean values for the PMM

( M = 57.95) were lower than that of GMM ( M = 81.90), and there was a significant

difference between the groups ( p < 0.05). The direction of the results was opposite to the

hypothesis).

Test 7--Total final exam grades. Appendix F shows the mean values and standard

deviations for the total final exam grades. According to the results, mean values for the

PMM ( M = 63.49) were higher than that of GMM ( M = 56.61), and there was a

significant difference between the groups ( p < 0.05). Hence, the hypothesis was accepted



47

(Final Exam Grade = [Final MCQ + Final Essay] ×100/90).

Table 4

Means and Standard Deviations for Final Exam MCQ Grades of Personalized


___________________________________________________________________________


___________________________________________________________________________



_______________________________________________________________

Note. p < 0.05.

Summary

This chapter presented the t test results of data (grades) gathered by five

instruments (tests) from two groups of students who underwent two different treatments:

PMM and GMM. Further, the chapter presented two calculated grades (total grade, total

final exam grade) and relevant t tests on those grades.

Of seven t tests conducted, two tests (final MCQ grades and final exam grades)

supported the hypothesis, The effectiveness of personalized messages is greater than that

of group messages in motivating students, whereas four tests (midsemester grades, case

presentation grades, final essay grades, and total grades) rejected the hypothesis. A

seventh test (attendance grades) also rejected the hypothesis; however, it indicated that

there was a significant difference between two groups' grades. The following chapter

discusses these results and makes conclusions that may be drawn from them.



48

Chapter 5: Discussion of Results

Introduction

This chapter contains seven major sections. The first section introduces the

chapter. In the second section, the study’s research problem and method, are briefly

summarized. In the third section, results of the study’s five tests are discussed. The fourth

section discusses results of two additional tests. The fifth section suggests and discusses

limitations of the study. The sixth section offers suggestions for further research. The

final section summarizes the chapter.

Motivation, ARCS Model, E-Mail, and SMS

For many years, it has been generally accepted that communication and

motivation affect students’ learning. Properly designed communication methods can

enhance students’ motivation to learn. This paper described a study to determine whether

a personalized MMS was more effective than a group MMS, at a School of Computing

and Information Technology. A specific course (Business Information Management

Systems) was selected to test whether the above statement was, in fact, true.

Forty-four students were randomly selected and divided into two groups. All the

students were part-time, undergraduate, final, fourth-year students.

Two methods were used to deliver the MMS to students: text messages on mobile

phones (SMS) and e-mail. E-mail was used to deliver the messages only when the length

of the message was more than 160 characters or the messages contained graphics.

Keller’s (1984) ARCS model of motivation was used to design the MMS.

The students’ grades for five measures were used as the motivational levels of

students. Tests were spread from the middle to the end of the semester.



49

Discussion of Results--The Research Question Against Five Tests

Test 1--Midsemester multiple-choice quiz. The directional hypothesis was, The

effectiveness of personalized messages is greater than that of group messages in

motivating students. This study found no significant difference between the midsemester

test grades of students who received PMM and those who received GMM. This finding is

consistent with the literature of the MMS developed using ARCS model. L. Visser (1998)

and L. Visser et al. (2002) found that the PMM and GMM equally motivated the distance

learning students.

In this study, students had received only four motivational messages before the

midsemester test. Hence, the lack of significant difference between the groups may be

due to the fact that the number of messages was too small to make a significant difference

in motivating students.

Test 2--Case presentation. This study found no significant difference between the

case presentation grades of students who received PMM and those who received GMM.

This finding is consistent with the literature of the MMS developed using ARCS model.

In this study, students received 10 messages out of 12 messages before they did the case

presentation. Although the case presentation grading may be highly subjective

(depending on who grades them), the students in this study were graded by only one tutor

who used a standard rubric for grading.

Test 3--Final exam multiple-choice test . As hypothesized, this study found a

significant difference between the final exam multiple-choice test grades of students who

received PMM and those who received GMM. Students received all the 12 messages

before they sat for the final exam MCQ test, and the test consisted of only

multiple-choice questions. The higher test scores of those receiving PMM may be



50

explained in three ways. First, the number of messages sent to students before they took

the test (students received all 12 messages before the test) possibly affected the highest

level of motivation. Second, the nature of the test itself (the multiple-choice tests were

highly objective as they were marked by the computer) caused subjectivity to be

eliminated. Third, the timing of the last message (“Peter, good luck in your final exam.

Damith”) was sent to students’ mobile phones a few minutes before the final exam

started. This was the most effective message (according to students’ e-mail feedback after

the final exam) as it was received at a time when they were very nervous and

psychologically down. Receiving a personalized message from the tutor wishing them

good luck might have motivated them more than those who received a common group

message on their mobiles.

Test 4--Final exam essay test . This study found no significant difference between

the final exam essay grades of students who received PMM and those who received

GMM, and the hypothesis was rejected. Although this finding was consistent with the

literature of the MMS developed using ARCS model, the nature of the test (subjectivity)

might have had effects on these results that were different from those of the final exam

MCQ part. The final exam MCQ test found significant difference between the two groups

(this result was counter to or inconsistent with the results found with final exam MCQ

test).

Test 5--Total grade. This study found no significant difference between the total

(full course) grades of students who received PMM and those who received GMM. The

total grade is the final average consisting of all the course work grades (three tests) and

final exam (two tests) grades.

Because the course work was done throughout the semester, students had not



51

received all the messages before they started the course work tests. Only the final exam

grades indicated the full effects of all the messages. This finding was consistent with the

literature of the MMS developed using ARCS model (L. Visser, 1998; L. Visser et al.,

2002).

Discussion of Results--Additional Analysis Using Two Tests

In an attempt to better understand apparent differences in the performance of the

two groups of students in this study, additional analysis was conducted on students’

attendance and their combined score on the final exam.

Test 6--Attendance. This study found a significant difference between the

attendance grades of students who received PMM and those who received GMM. Quite

unexpectedly, students who received personalized messages did not attend as many

classes as those who received group messages, with a significant difference in their

attendance. This difference may be explained by the fact that the students who

participated in this study were part-time, evening students who had their full-time jobs

during the day. When considering these circumstances, if the students received

personalized attention from the tutor (using e-mail or SMS), they may have thought that

attending regular tutorial classes was unnecessary, resulting in poor attendance grades.

Test 7--Total final exam. This study found no significant difference between the

total final exam (total of final essay and MCQ) grades of students who received PMM

and those who received GMM. Hence, the hypothesis was rejected. This finding was

consistent with the literature of the MMS developed using the ARCS model. This test

was conducted because (final exam MCQ test) of the final exam grades showed a

significant difference, whereas the other part (final exam essay) grades showed no

significant difference between PMM and GMM.



52

Limitations of the Study

There were at least five limitations in this study. Those were

1. No pilot testing of messages.

2. The researcher’s expectations.

3. Students’ possible bias.

4. Nature of the instruments.

5. No posttreatment measures.

No pilot testing of messages. The treatment (the motivational messages system)

was not piloted to measure the validity of the messages. Pilot testing of the messages

could have eliminated the design flaws and, finally, the effectiveness of the MMS.

The researcher’s expectations The researcher’s expectations could have been a

contaminating influence in the findings. The researcher was the tutor for both groups and

might have had a biased attitude toward the research and possibly “worked” toward a

positive outcome of this study.

Students’ possible bias. Although students were not told that there were two

groups receiving two different treatments, students became aware of the differences in

the middle of the course. Hence, knowing that one set of students was receiving

personalized messages from the tutor, students in the personalized message group may

have led students to work harder to get better grades than others. This is similar to the

Johan Henry effect or the Hawthorne effect that are threats to the internal validity of the

study.

Nature of the instruments. Five instruments were used to measure the students’

achievement (grades): midsemester MCQ test, case presentation, attendance, final exam

MCQ, and final exam essay that indirectly measured the motivation levels. Except for the



53

midsemester MCQ and final exam MCQ, all the other tests were highly subjective,

depending on who evaluated the students. In the case presentation, the same tutor

(researcher) evaluated all the participants in both groups, thereby, minimizing

subjectivity. But the final exam essay test was marked by five different tutors assigned

randomly. Hence, high subjectivity on these test grades might have threatened the

validity of the instrument, but the use of same rubric (model answers) by all the tutors

reduced the effect.

No posttreatment measures. No effort was made in this study to see whether

students really read their SMS and e-mails, or, if they did, what percentage of students

actually read them. Posttreatment fact-finding procedures such as focus group discussion

could have added useful qualitative data to improve the validity of the findings.

Suggestions for Further Research

Further research is justified for two reasons: first, because of the unexpected

findings of better attendance among group MMS recipients and, second, because of the

limitations listed above.

The first suggestion is to repeat the same study with a posttreatment focus group

discussion to determine whether students really read the SMS and e-mail and if they were

(or were not) motivated by the messages. If the students did not read the messages, it

would be useful to determine why they did not.

The second suggestion is to conduct this study with full-time, day students to see

whether attendance grades would differ from the results of this research (this study found

significant difference between the two groups, and the personalized MMS group’s

attendance was poorer than that of the MMS group. This should lead to a further,

interesting research to find if MMS can encourage the students to do self-study (without



54

attending classes), given the guarantee that they can have access to their tutor on a

personal basis. Although this is not the real situation (students do not communicate with

the tutor much), they perceive (due to PMM) that they can do so.

Summary

This chapter discussed results to answer the study’s research question against

seven tests as well as subsequent analyses that addressed issues raised during the

implementation of the MMS. Findings of no significant difference among students in four

test grades were consistent with MMS literature. The existence of significant difference

among students in attendance grades, final exam grades, and final MCQ grades would

seem to conflict with the MMS literature.

Although it was not the objective of this study to determine what number of

messages would be most effective in motivating students, the comparison of grades

between midsemester MCQ and final exam MCQ suggests that 12 messages are more

effective than 6 messages.

This study was conducted to determine whether the personalized MMS can

significantly increase the students’ achievements than the group MMS can. Although the

results suggested that there was no significant difference between both methods’

capabilities of increasing students’ achievement, its findings also suggested that further

investigation is warranted.



55

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62

Appendix A

Motivational Design Activities and Process Questions



63

Motivational Design Activities and Process Questions

______________________________________________________________________________________

Activity Process questions______________________________________________________________________________________

Define

1. Audience motivation analysis What are the audience’s motivational attitudes

toward the course to be offered?

2. Motivational objectives What do I want to accomplish with respect to the

motivational dynamics of the audience?

3. Motivational criterion measures How will I determine whether I have accomplished

my motivational objectives?

Design

4. Generate potential strategies How many possible strategies are there that might

accomplish the motivational objectives?5. Select strategies Which strategies seem to be most acceptable for

this audience, instructor, and setting?

6. Integrate strategies How do I combine the instructional and

motivational components into an integrated

design?

Develop

7. Prepare motivational materials How do I locate or create motivational materials toachieve the objectives?

8. Enhance existing instructional materials How do I rework the instructional material toimprove its motivational appeal?

9. Developmental test How can I get feedback as to whether these

motivational strategies are likely to work?

Pilot

10. Implement with target population How do I prepare for and conduct a pilot test with

representatives of the target population?

11. Evaluate effects How can I detect the expected and unexpectedmotivational effects of the course?

12. Certify or revise How do I determine whether the course should berevised or go on-line?

______________________________________________________________________________________



64

Appendix B

Group and Personalized Messages



65

Group Messages

Message#

Message Message type

SMS E-mail

1

Welcome to the beginning of the semester. We willcommunicate with you mostly by e-mail and SMS for

the duration of this course.

IMPORTANT!

Please respond to this test message to acknowledge that

you are receiving the messages. Thank you.

•

2Time management is very important in DBMS. Do not

procrastinate in your assignments.•

3 Your test is in this week (5). •

4You should cover the material we went over in class for

the test. Test will be on Unit 1 & 2• •

5 The better your course work grade is, the better yourchances for success is. Study hard for the test.

•

6Remember to focus on the assignment. Are you having

trouble with the assignment?•

7 Remember to prepare for your presentation on Thursday. •

8

This is about the middle of the semester. We have come

a long way. What do you think of DBMS so far?

Moreover, how would you make it better?• •

9

I want to stress the importance of meeting deadlines.Please keep focused. Nevertheless, give yourself a few

minutes of downtime to clear your mind when things get

too hectic.

•

10

If you have any weaknesses or problem areas in the

subject, let me know. •

11 Good Luck in your Final exam •



66

Personalized Messages

Message#

Message Message type

SMS E-mail

Sameas

Group

1

Welcome to the beginning of the semester. Wewill communicate with you mostly by e-mail and

SMS for the duration of this course.

IMPORTANT!

Please respond to this test message to

acknowledge that you are receiving the messages.

Thank you.

• •

2<Peter> Time management is very important in

DBMS. Do not procrastinate in your assignments.•

3 Your test is in this week (5). • •

4You should cover the material we went over in

class for the test. Test will be on Unit 1 & 2 • •

5-A

The better your course work grade is, the better

your chances for success is. Study hard for the

test.• •

5-B<Peter> You got <90> marks for the test-1.

Congratulations!•

6Remember to focus on the assignment. <Peter>

are you having trouble with the assignment?•

7-ARemember to prepare for your presentation on

Thursday.• •

7-B<Peter> You did <Excellent> in your presentation

<Yesterday>. You got <85>.•

8

This is about the middle of the semester. We have

come a long way. What do you think of DBMS so

far? Moreover, how would you make it better?• •

9

<This message will give students brief idea about

their individual performance so far in the course.Highly personalized>

• •

10

If you have any weaknesses or problem areas in

the subject, let me know. • • •

11 Good Luck < Peter> , in your Final exam •

Notes: 1. Words in side < > may change according to the situation/person2. Message numbers with “-B” are only used in personalized messages to

give individual students a feed-back/grade on their performance. (e.g. 5-B)3. The last column “same –as Group” indicates whether, message is

personalized or not.





68

Means and Standard Deviations for Final Exam

Essay Grades of PMM Group and GMM Group

___________________________________________________________________________


___________________________________________________________________________



_______________________________________________________________

Note. P > 0.05.



69

Appendix D

Means and Standard Deviations for Total Grades of PMM Group and GMM Group



70

Means and Standard Deviations for Total Grades of PMM Group and GMM Group

___________________________________________________________________________


___________________________________________________________________________



_______________________________________________________________

Note. P > 0.05.



71

Appendix E

Means and Standard Deviations for Attendance Grades of PMM Group and GMM Group



72

Means and Standard Deviations for Attendance Grades of PMM Group and GMM Group

___________________________________________________________________________


___________________________________________________________________________



_______________________________________________________________

Note. P < 0.05.



73

Appendix F

Means and Standard Deviations for Total Final Exam Grades

of PMM Group and GMM Group



74

Means and Standard Deviations for Total Final Exam Grades

of PMM Group and GMM Group

___________________________________________________________________________


___________________________________________________________________________



_______________________________________________________________

Note. P < 0.05.

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