Technology Priorities and Preferences of Developmental ... · Technology Priorities and Preferences...

27Technology Priorities and Preferences

Society relies on technology. Thus, it would seem natu-ral that technology would be integrated into education classrooms. Educators have recognized that technology can transform learning. In 1989, the National Council of Teachers of Mathematics (NCTM) supported the use of technology in the K–12 mathematics curriculum and classrooms and stated that calculators and computers should be available to all students and in all classrooms for demonstrative and investigative purposes. Over the next two decades, mathematics associations supported the use of technology as an aid for teaching and learn-

Technology Priorities and Preferencesof Developmental Mathematics Instructors

Linda ZientekSusan T. SkidmoreD. Patrick SaxonStacey Edmonson

Linda Reichwein Zientek is an Associate Professor in the Department of Mathematics and Statistics at Sam Houston State University

Susan T. Skidmore is an Associate Professor in the Department of Educational Leadership at Sam Houston State University.

D. Patrick Saxon is an Associate Professor in the Department of Educational Leadership at Sam Houston State University.

Stacey Edmonson is the Dean of the College of Education and Professor in the Department of Educational Leadership at Sam Houston State University.

With the omnipresence of technology in society came the inevitable integration into education. This manuscript pro-vides results of a statewide survey of developmental math-ematics instructors’ technology preferences and priorities across all levels of developmental mathematics courses. The study was part of a larger project designed to explore the use of technology in developmental education instruction. Educators and administrators can utilize this information to prepare professional development opportunities and to as-sist in their understanding of popular and appropriate tech-nology to apply in developmental mathematics instruction. The information might also be used to garner ideas to aid in compliance with legislative mandates on instructional tech-nology. The results suggest that developmental mathematics instructors were familiar with and considered educational technology a priority. However, full-time instructors were more likely than part-time instructors to consider technol-ogy a priority. Many instructors consider commercialized content-based instructional software and calculators as the most important technology for content delivery. Keywords: technology, developmental mathematics, education

28 The Community College Enterprise • Spring 2015

ing at the postsecondary level (American Mathematical Association of Two-Year Colleges [AMATYC], 1995, 2006; Mathematical Association of America, 2003). The present research examined developmental mathematics instructors’ self-re-ported familiarity with educational technology and discovered their top technol-ogy applications of choice.

It is unreasonable to assume that every student pursuing higher education is fully prepared for success at the outset. Developmental education classrooms provide students who are underprepared for college the opportunity to gain the skills needed for college-level courses. The proportion of students who enroll in developmental mathematics has been higher than in reading and writing. A higher proportion complete their mathematics remediation at two-year institu-tions than four-year institutions (Parsad, Lewis, & Greene, 2003). Data collected from 1,186 postsecondary institutions indicated that the percent of students re-quiring developmental mathematics remained unchanged from 1995 to 2000 (i.e., 22%) with 35% of students enrolled at public two-year colleges requiring remediation (Parsad et al., 2003). Though the figures are somewhat dated, there is no evidence to suggest that these numbers have declined to any great extent. Even though the composition of developmental mathematics classrooms can be diverse, Radford, Pearson, Ho, Chambers, and Ferlazzo (2012) found certain characteristics that suggest a higher likelihood of being enrolled in a develop-mental education course. These characteristics included not being enrolled in a degree program, not completing a mathematics course higher than Algebra II, and having been out of high school for more than one year. The latter supports findings by Zientek, Schneider, and Onwuegbuzie (2014), who noted that the top two reasons faculty believed students were placed in developmental mathe-matics courses were a “time delay from previous mathematics course” and a “lack of basic math skills” (p. 72). In addition, Radford et al. (2012) found a higher proportion of Blacks and Hispanics were enrolled in developmental mathemat-ics courses than Whites and Asians. Regarding who is teaching the students, Shults (2001) found that the majority, about 65%, of developmental education faculty were classified as adjunct or had a part-time status.

Integration of Technology in Mathematics InstructionA review of the literature suggests that empirical research on technology in devel-opmental mathematics courses has been sparse. The majority of technology re-search published in peer-reviewed journals has been in K–12 settings. Therefore, the following discussion regarding technology in K–12 classrooms begins with the assumption that many of the benefits and barriers associated with technology might be generalized to college classrooms.

Technology can transform teaching and learning. In order to be effective, technology must be integrated with “curriculum, instruction, and assessment” (Berlin & White, 1995, p. 51). Technology can be used to “learn content and skills,” to “complement or enrich the curriculum,” and in “transformative ways” (Ertmer, Ottenbreit-Leftwich, Olgun, Sendurur, & Sendurur, 2012). The number and


variety of technology tools used in the mathematics classroom have continued to evolve. In 2006, AMATYC provided a comprehensive list of technology tools that might be used mathematics classrooms. This list included “graphing calcu-lators, student response systems, online laboratories, simulations and visualiza-tions, mathematical software, spreadsheets, multimedia, computers or the Inter-net, and other innovations yet to be discovered” (p. 55). Concerning the use of computers, postsecondary institutional responses in a study conducted in the fall of 2000 indicate that 29% of institutions never or very rarely had developmental mathematics students use computers as a hands-on instruction tool and 40% answered occasionally used and 31% answered frequently used (Parsad et al., 2003). Bos (2009) identified typical methods for applying technology in math in-struction. These included game, informational, quiz, static tools, and interactive formats. Cognitive fidelity has been defined as “whether a concept is better un-derstood when the object is acted on” (Bos, 2009, p. 111). Most applications had low cognitive fidelity levels with activity mats categorized as medium-high and in-teractive formats categorized as high. Therefore, merely introducing technology in the classroom or in assignments will not guarantee that learning is improved.

Developmental Mathematics CoursesCourse redesign. Technological innovations expanded the instructional

format options (Kinney & Robertson, 2003) and provided the opportunity to redesign the delivery of developmental education. Technology allows for modes of instruction to be synchronous, asynchronous, or both and enables distance learning and computer-mediated learning. Course redesign success stories have been published in the literature (National Center for Academic Transforma-tion, 2012; Vandal, 2011). Even though the evolution of technology has enabled instructors to deliver online distance-learning or hybrid courses, the research in this area is in the elementary stages and is not the focus of this manuscript (Ashby, Sadera, & McNary, 2011; Bendickson, 2004; Weems, 2002).

Some researchers believe there is a lack of rigorous efficacy research in devel-opmental courses (Rutschow & Schneider, 2011; Zavarella & Ignash, 2009). As noted by Zavarella and Ignash (2009), “far less research has investigated the effec-tiveness of computer-based instruction specifically for students in developmental education” (p. 2). However, in recent years, several studies have been conducted in developmental mathematics classrooms. In particular, computer-assisted in-struction has been investigated. Three studies reported no statistically significant differences on mathematics achievement between students who received com-puter-assisted instruction and those who did not (Gavitt, 2010; Spradlin, 2009; Taylor, 2008). Those results suggest that even though the use of computer-assist-ed learning did not increase achievement, student performance measures did not decrease. In other words, either computer-assisted learning or the traditional approach seemed appropriate. Despite no statistically significant differences be-tween the two groups, Taylor (2008) reported that students who utilized comput-er-assisted learning had a decrease in mean mathematics anxiety scores compared


with students who were taught in classrooms without computer-assisted learning. Also, Shah (2009) found promising results for the use of a learner-centered web-based intelligence system for improving student achievement.

One problem with evaluating the success of course redesigns has been that attrition rates have not always been considered. The importance of attrition rates for distance-based courses that are taught with technology was highlighted by Ashby et al. (2011). They found “distance-based and blended students performed worse than the traditional face-to-face developmental math students when not taking attrition into account, however considering only students who completed the course, face-to-face students performed worse” and noted that “student reten-tion cannot be disregarded” (p. 138). In the Zavarella and Ignash (2009) study, students enrolled in traditional lecture courses were less likely to withdraw than students enrolled in a computer-based format. In addition, Duka’s (2009) results were promising for the use of MyMathLabTM (i.e., commercially available soft-ware and online resources for mathematics instruction). However, Duka (2009) did not include students who did not take the final or “students who did not complete more than 25% of their homework in” the MyMathLabTM section (p. 14). Researchers should consider whether student attrition rates are important to the assessment of the use of instructional technology.

Handheld technology. Research conducted across precollege and college mathematics courses provide evidence that students can benefit when calcula-tors are used in instruction and assessment (see Burrill, Allison, Beaux, Kastberg, Leatham, & Sanchez, 2002; Ellington, 2003, 2006; Shore, 1999). In a review of research studies conducted in secondary mathematics classrooms, Burrill et al. (2002) found that handheld graphing technology can aid students in their learn-ing, but the way the technology is used, approached, and integrated should be considered. Students need to avoid an over-reliance on technology.

The use of handheld technology has been declared a suitable tool for teaching and learning in developmental mathematics classrooms (Laughbaum, 2002), but there is a limited amount of research in this area. Hollar and Norwood (1999) found that handheld technology can benefit intermediate algebra students. In their work, students who were taught with a graphing calculator approach to the curriculum exhibited a better comprehension of functions than students who were taught in a traditional classroom. However, no statistically significant differences existed between these students’ final exam scores or mathematics at-titude scores. The literature suggests that more research is needed on the use of handheld technology in developmental mathematics classrooms.

Classroom response systems. In mathematics courses, several studies have been published on the use of classroom response systems (CRSs), which are also referred to as clickers. A bibliography of studies has been provided by The Center for Teaching (2012). CRSs allow instructors to instantly assess students’ knowledge. Although the literature suggests CRSs improve student comprehen-sion, there is agreement that the existing evidence is not sufficient to support a scientific conclusion that CRSs benefit student learning.


In mathematics classrooms, the research primarily has been focused on calculus-based or statistics courses (The Center for Teaching, 2012). However, Blodgett (2002) conducted a study on CRSs in college algebra, which typically is the course that follows intermediate algebra. Blodgett (2002) detected no statisti-cally significant differences in student achievement between classes that utilized CRSs and those that did not utilize CRSs; however, students perceived CRSs as beneficial. Then again, college-level courses usually have a predetermined cur-riculum with a fast-paced schedule. Blodgett (2002) concluded that for the tradi-tional college algebra course, the “time involved with incorporating an interac-tive student response system . . . is not an ideal setting for an interactive student response system . . . ” (p. 70). Blodgett’s conclusion supported that of Caldwell (2007) which suggests that CRSs tended to result in less content coverage. In ad-dition, King and Robinson (2009) concluded that pedagogical changes need to be made when using CRSs. Developmental mathematics courses typically also have a predetermined curriculum, which might impact the ability to use CRSs in any significant capacity.

Internet-based blogs and chats. The need for students to “analyze and evaluate the mathematical thinking and strategies of others” has been advocated by NCTM (2000, p. 60). Blogs and chats are relatively new applications that can be used as learning tools. Empirical research on the use of blogs in developmen-tal mathematics classrooms is lacking. However, Cooper (2012) noted that the introduction of blogs and forums opens the possibility of encouraging communi-cation by incorporating writing within the mathematics curriculum. In a sociol-ogy course, Pearson (2010) found that the use of blogs could aid in identifying students’ level of understanding, which improved discussions during class time.

Barriers exist that might deter the introduction of Internet-based blogs and forums into the mathematics curriculum. Cooper (2012) concluded that “one likely question in this discussion is whether this Internet-based, informal writing can really be considered writing” (p. 84). A second concern identified by Cooper (2012) was that teachers might not utilize technology that can increase writing in mathematics because they are not sure of the worth or are simply uncomfort-able using technology. Pearson (2010) did note some problems with using blogs in a sociology course. These included difficulty accessing and posting to the site, remembering to post by a particular due date, and formulating new ideas in the course of reviewing previously published postings. Because blogs and chat forums are relatively new, the research in this area will need to evolve.

Professional DevelopmentThe successful integration of technology will depend on teachers’ pedagogical beliefs. Ertmer (2005) surmised that in order for pedagogical beliefs to change, teachers need to have both personal and vicarious experiences. Professional de-velopment can provide pedagogical-changing experiences, but will need to be rigorous and attentive to the needs of the postsecondary mathematics instructors (Epper & Baker, 2009). The importance of providing professional development


for the successful integration of technology has been acknowledged by several re-searchers, although their focus was on high school mathematics (see Burrill et al., 2002; Heller, Curtis, Jaffe, & Verboncoeur, 2005). From their review of research, Burrill et al. (2002) concluded “that simply providing teachers with information about how the technology functions is not likely to result in effective integration in the classroom” and “substantial professional development and support is nec-essary for teachers to make informed decisions about how to best use handheld technology in their classrooms” (p. i).

In addition, the application and integration of instructional technology might also be affected by developmental faculty employment status. As is the case for the field in general, most developmental mathematics courses are taught by adjunct faculty (see Gerlaugh, Thompson, Boylan, & Davis, 2007). Boylan and Saxon (2012) documented several challenges to campus and program engage-ment that have been encountered by some adjunct faculty. Spending less time on campus was one challenge that might limit these faculty members’ participation in discussions about technology applications and usage. Adjunct faculty might also be less likely to engage in professional development activities. These chal-lenges might impact adjunct faculty propensity to learn and apply technology in support of developmental mathematics instruction.

PurposeIn Texas, where this study was conducted, House Bill (HB) 1244 requires the in-tegration of technology in developmental education courses. As legislators begin making education mandates that impact classroom practices, they must realize that the success of these directives will depend on faculty members’ beliefs and practices. The purpose of this statewide study was to gain information from de-velopmental mathematics faculty at postsecondary institutions on their priorities, familiarity, and preferences pertaining to the integration of technology. For this study, the K–12 definition for integrating technology by Hew and Brush (2007) was modified: “Technology integration is thus viewed as the use of computing devices” in postsecondary institutions of education “for instructional purposes” (p. 225), which includes content-delivery. The present study builds off of the Developmental Education Program Survey (DEPS), which was administered by the Texas Higher Education Coordinating Board (THECB; 2011).

A unique characteristic of the Developmental Education Technology Survey (DETS, Skidmore, Saxon, Zientek, & Edmonson, 2012), from which the current study derives its data, compared to that of the initial DEPS, is that information was collected from individual developmental mathematics faculty rather than from a single institutional contact person. Participants responded to a survey regarding the use of technology in their own developmental education classroom. The following research questions were investigated with differences between teaching status examined:

• To what extent do developmental mathematics instructors prioritize


the incorporation of technology in their developmental education classrooms?

• How familiar are developmental mathematics instructors with the ap-plications and uses of technology in the learning environment?

• What are developmental mathematics instructors’ preferences for (a) hardware and commonly used software, (b) content-delivery tools, (c) online resources, (d) communication tools, and (e) collaborative on-line tools?

Method

SampleThe sample for the present study is a subset of the sample from the DETS which was sent to the 98 institutions that participated in the DEPS. For mathematics, 379 developmental mathematics faculty members from 55 institutions (56%) responded to the DETS. Within institution response rates were not available. Two-thirds of the faculty respondents were female, and 87% were from a 2-year or technical college. Of the 356 faculty who reported their teaching status, roughly half of the respondents indicated they were full-time (51% and 49%, full-time and part-time, respectively). The majority (65%) of the respondents had not taught an online or hybrid course within the past three years. Of the fac-ulty members who had taught an online or hybrid course within the past three years, the majority (70%) were required to complete training prior to teaching the course. A few faculty members (15%) indicated they also had duties as an ad-ministrator. Almost two-thirds of the respondents had master’s degrees, a quarter had bachelor’s degrees, and a tenth had doctoral degrees. The majority (62%) of respondents indicated they had taught as a certified teacher in any grade level in K–12. At least 62% had a minimum of six years of experience teaching develop-mental education at a community college.

InstrumentThe purpose of the DETS was to understand developmental education instruc-tors’ current instructional technology practices. The DETS built off of the ex-isting DEPS, which “was designed to better understand how developmental education is performed across the state and at individual institutions” (THECB, 2011, p. 1). The instrument initially was developed by the research team by iden-tifying relevant factors from the literature and expanding upon the technology questions on the DEPS. Researchers convened with representatives from each of the three advisory colleges who were content experts in their teaching field. Advisory teams, which were comprised of developmental education instructors and administrators, iteratively refined and revised the questionnaire items. After consensus was reached, the final version of the DETS was distributed. Ques-tions analyzed in this study are included in the Appendix. A contact person at


each institution was invited to participate and was asked to distribute an e-mail invitation containing a link to the DETS to all full- and part-time developmental instructors. To examine differences between full-time and part-time instructors and differences between institutional and departmental policy requirements, t tests for differences in means were conducted.

Results

Research Question I: Use and PrioritizationThe majority of participants (82%, n = 377) indicated that they use instructional technology in the developmental education classroom. When asked about the extent to which they prioritized the incorporation of technology in their class-rooms, more than half (63%, 232 of n = 367) of the respondents marked a 4 or a 5 on a scale of 1 to 5, where 1 = “not a priority” and 5 = “an essential prior-ity.” Statistically significant differences existed between full-time and part-time instructors on (a) their reported use of instructional technology (χ2

1 = 11.52, p

< .01) 89% (Did use, n = 161; Did not use, n = 20 ) and 75% (Did use, n = 130; Did not use, n = 43), full-time and part-time, respectively, and (b) ratings on their use of technology in the classroom as essential (t

343 = 3.12, p < .01; Cohen’s d

= .33; MFT

= 3.91, SDFT

= 1.31; MPT

= 3.46, SDPT

= 1.39; full-time and part-time, respectively).

Local policies. Because policies would impact use of technology, instructors were asked to rate the extent to which (a) institutional/campus policies and (b) division/departmental policies “require or not require the use of technology in the development education classrooms?” Responses were rated on a 5-point scale from 1 = “require” to 5 = “not required.” A paired samples t test indicated statisti-cally significant differences existed between instructors’ reported institutional/campus and departmental/division policy requirements (t

367 = 5.73, p < .001;

Cohen’s d = 0.30; MIC

= 2.99, SDIC

= 1.41; MDD

= 2.62, SDDD

= 1.45, institutional and departmental, respectively).

Research Question II: Familiarity With TechnologyInstructors’ responses indicated that instructors perceive themselves as familiar with using technology to promote student learning (M = 2.19, SD = 0.97, 95% CI [2.09, 2.28], n = 378). When asked to rate their familiarity with applications and uses of technology in the learning environment, almost two-thirds (63%, n = 239) rated themselves a 1 or 2 on a scale of 1 to 5, where 1 indicated “extremely familiar” and 5 indicated “not at all familiar.” Approximately 10% (n = 39) of the respondents rated themselves a 4 and one rated a 5. Statistically significant differences did not exist between full-time and part-time instructors on their fa-miliarity with technology (t

353 = 1.73, p = .08; Cohen’s d = 0.19; M

FT = 2.09, SD

FT

= 1.01; MPT

= 2.27, SDPT

= 0.93, full-time and part-time, respectively).


Research Question III: Most Important Instructional TechnologyInstructors were asked to identify their top choice for the most important instruc-tional technology tool used in their developmental mathematics courses across five categories (see Appendix). Differences between full-time and part-time in-structors were investigated.

Hardware or commonly used software. As seen in Figure 1, when graph-ing and non-graphing calculators were combined together, the calculator was the top choice as the most important hardware or commonly used software in de-velopmental mathematics classrooms. However, 23% (41 out of 182) of full-time and 26% (45 out of 174) of part-time instructors chose “not applicable” for this category. Even though the use of tablets was the only category where differences existed between full-time and part-time instructors, tablets were the top choice by only 3% (n = 5) of full-time instructors and by none of the part-time instruc-tors. Of the faculty who chose “not applicable” for hardware or commonly used software, 64% (62 out of 97) had marked a 3, 4, or a 5 on the question about institutional/campus policies and 56% (52 out of 93) marked a 3, 4, or a 5 on division/departmental policies on a 5-point Likert scale, 1 = “require” to 5 =

“not required.”

Content-delivery and communication tools and online resources. As seen in Figure 1, “commercialized content-based instructional software products” was developmental mathematics instructors’ top choice as the most important content-delivery tool used for both full- and part-time instructors. As seen in Figure 1, most instructors chose “not applicable” when asked to provide their top choice as the most important online resource, with a larger proportion of part-time instructors than full-time instructors choosing this option. The next most important online resource chosen by both full- and part-time instructors was “online tutoring.” A larger proportion of full-time instructors than part-time instructors chose “websites.”

Communication tools. “E-mails about course communications” (i.e., course progress, course feedback; 40%, 73 out of 182, and 35%, 61 out of 174, full-time and part-time instructors, respectively) and “online grade/performance updates” (32% or n = 58 and 35% or n = 61, full-time and part-time instructors, respec-tively) were developmental mathematics instructors’ top choices for the most im-portant tools used to facilitate communication between instructors and students.

“E-mails about services” (tutoring, special events, etc.) was chosen by 8% (n = 15) of full-time and 12% (n = 21) of part-time instructors.

Collaboration tools. The majority of instructors did not select a top choice for the “most important online collaborative tool used” and opted to choose

“not applicable” (67%, 122 out of 182, and 74%, 128 out of 174, full-time and part-time instructors, respectively). Another 9% (n = 16) of full-time instructors and 13% (n = 22) did not respond to this item. However, 18% (n = 32) of full-time and 9% (n = 16) of part-time chose “discussion boards.” The other options


that were selected by a trivial number of instructors were “blogs,” “social media,” “videoconferencing,” and a “wiki.”

DiscussionThe high proportion of students who are placed in developmental education courses is a concern at the national level. Increasing the number of students who obtain a college degree is contingent on the successful remediation of these students. The effective use of instructional technology has been accepted as a tool that can increase student learning at the K–12 level (see Ozel, Yetkiner, & Capraro, 2008), and mathematics associations agree and provide statements of support for the use of technology in K–12 and college classrooms (AMATYC, 1995, 2006; Mathematical Association of America, 2003; NCTM, 1989). There-fore, it is a logical assumption that instructional technology could have a positive impact on student learning in developmental mathematics classrooms. In Texas, where the current study was situated, the use of instructional technology in de-

Figure 1. The most important instructional technology tool by category, in develop-mental mathematics courses, as a percentage of full-time and part-time instructors.


velopmental education classrooms has been mandated by the state. Mandates alone will not lead to the effective use of technology in these classrooms and tech-nology research is lacking in these classrooms. Because the instructor ultimately decides the classroom practices, a study that collects information on instructors’ priorities, familiarity, and preferences regarding the integration of technology in the developmental education classroom will help set a foundation for profes-sional development training and for future research studies.

By identifying instructors’ top choices of instructional technology applica-tions, and by gauging their familiarity with technology, administrators can plan for training and budget for technology purchases. Researchers can utilize this information when determining the types of technology on which to focus in future studies; and instructors can learn about their colleagues’ choice of tech-nology across various campuses. Results indicate that (a) policies on the use of technology in developmental mathematics classrooms were more likely to be at the departmental level than the institutional level; (b) developmental education instructors were familiar with and considered educational technology a priority; (c) full-time instructors were more likely than part-time instructors to consider technology a priority, which suggests a discrepancy between instructor status; (d) of those who selected a hardware or software tool, some type of calculator was the top choice; (e) full-time instructors were more likely to select commer-cialized content-based instructional software products as their top choice as the most important content-delivery tool; and (f) many, although not a majority of, instructors chose the response that online resources were not applicable in devel-opmental education classrooms.

Prioritization of and Familiarity with TechnologyInstructors’ use of technology will likely relate to their prioritization of and fa-miliarity with technology. The majority of developmental mathematics instruc-tors in our study reported that (a) technology was a priority, (b) they had a high degree of familiarity with technology, and (c) they used instructional technology. On the whole, these perceptions about technology were more prevalent in full-time instructors than part-time instructors. Thus, there appears to be an incon-sistency between full-time and part-time instructors on the use of instructional technology. Statistical tests and small effect sizes indicated requirements to use technology were more likely made at the departmental level rather than the insti-tutional level. With evidence that technology was valued by many instructors, we sought to discover instructors’ top choices of instructional technology.

Top Choice of Technology in Classrooms Because technology can be used for different instructional purposes, we disag-gregated technology use into several categories. Developmental mathematics in-structors then responded to their top choice of technology for each category. To determine the extent to which differences existed by teaching status, results were disaggregated by full-time and part-time instructors.


Hardware or commonly used software. Graphing or non-graphing cal-culators in their teaching were chosen by approximately 40% of instructors as their top choice of hardware or commonly used software. There was almost even division between graphing and non-graphing calculators. The results provide evidence that developmental mathematics classrooms can be a setting for future research on the use of calculators in the instructional setting. As seen in Figure 1, approximately 25% selected “not applicable” as their top choice of the most used hardware or commonly used software category. This suggests that many instructors prefer not to use software or hardware in developmental mathematics instruction.

Only a small percent of instructors chose Word, iPads/tablets/readers, pen-casts, or classroom response systems (CRSs). In light of previous research on CRSs (see Caldwell, 2007), there are several reasons CRSs might not have been chosen. First, class sizes in developmental mathematics, on average, tend to be small (i.e., 21 students; Gerlaugh et al., 2007). Second, instructors of tradition-al developmental mathematics courses might lack the flexibility to reduce the amount of content covered. If CRSs are to be used in developmental mathemat-ics courses, more research needs to be conducted to determine the extent to which (a) the curriculum is flexible enough to accommodate the time needed for incorporating CRSs (see Caldwell, 2007); (b) there are benefits to using CRSs in small classrooms; and (c) there are professional development opportunities avail-able to address the pedagogical changes that need to be made when using CRSs (see King & Robinson, 2009).

Content-delivery tools. As seen in Figure 1, full-time instructors were more likely than part-time instructors to select commercialized content-based instruc-tional software products as their top choice as the most important content-de-livery tool. In addition, 22% of part-time instructors marked “not applicable,” which suggests that part-time instructors might not be as likely as full-time in-structors to embrace instructional technology for content delivery. Given the widespread employment of part-time faculty in developmental education, this finding suggests that future research might want to focus on the effectiveness of content-delivery tools. To the extent that these delivery tools prove to be impor-tant to student learning, full-time instructors need to spend more time commu-nicating with part-time instructors on implementing these tools.

Communication. Evolutions in technology provide new opportunities for instructor-student interactions. Today, because of online handheld devices and wireless Internet, students instantaneously can access the Internet almost any-where to initiate communication with instructors. In the current study, regard-less of teaching status, respondents recognized the value of using technology as a communication tool. At least 85% chose one of the four options; 70% of instructors selected either “E-mails about course communications” (i.e., course progress, course feedback) or “online grade/performance updates” as their top choice.


According to Myers, Martin, and Mottet (2002) students’ motives for commu-nication with teachers are “relational, functional, participatory, excuse making, and sycophantic” (p. 126). Because e-mail is a popular mode of communica-tion, instructors should consider the reasons student choose to communicate by e-mail. In particular, instructors should begin to think about how the use of e-mail course communications (e.g., course progress, course feedback, etc.) and online grade updates can help facilitate student-instructor interactions. For example, Young, Kelsey, and Lancaster (2011) found that both teacher imme-diacy and the beliefs that teachers frequently e-mailed the entire class increased

“students’ positive predicted outcome value of fostering a student-teacher rela-tionship” (p. 383). Immediacy could be fulfilled in e-mails by including students’ names and emoticons. In addition, they found that students actually may “prefer inclusive, all-group/mass e-mail communication to targeted one-on-one emails” (p. 381). Other predictors in their model for fostering student-teacher relation-ships included “when students e-mail teachers for procedural/clarification rea-sons, and when students do not e-mail teachers simply as a means of efficiency” helped (Young et al., p. 381). Thus, because developmental mathematics instruc-tors were using e-mails to communicate about the course, future studies could investigate in detail the purpose for their e-mails, instructors’ interpretations of why they believe students communicate by e-mail, and if instructors believe e-mails are fostering positive student-instructor relationships.

Collaboration. Developmental mathematics instructors in this sample did not integrate technology as a collaborative tool in their classrooms as evidenced by the large percentage of instructors who marked “not applicable” (i.e., 67% and 74%, full-time and part-time instructors, respectively). A research topic that warrants further consideration is whether developmental mathematics instruc-tors might lack training on how to use technology for collaborative learning tech-niques. Furthermore, some of the contemporary course redesign methods such as modularized mathematics and computer-based mastery learning actually apply technology as such to preclude opportunities for collaborative learning.

Respondents also were not identifying blogs, videoconferencing, wikis, or so-cial media as a top choice for a collaborative tool, though as noted, the literature suggests that some of these tools might stimulate increased written communica-tion between students (Cooper, 2012; Pearson, 2010). A small percentage of the instructors selected discussion boards as their top choice for a collaborative tool. Future research could be conducted to determine if instructors teaching both de-velopmental and college-level math courses utilize collaborative tools differently depending on course level.

Online resources. As seen in Figure 1, the large percentage of instructors that chose “not applicable” in regards to online resources implies that many in-structors are not utilizing online resources in their developmental mathematics courses. This finding supports the conjecture that more training and professional development should be provided on how online resources can be incorporated in these classrooms. The large percentage of instructors who chose either online


tutoring or websites as their top choice for an online resource is encouraging because students can access these resources as needed. In addition, lessons and examples that are posted on the web allow students to replay the information as many times as needed and also allow them to learn from various instructors. Giv-en that full-time instructors are more likely than part-time instructors to choose websites as their top choice, more web resources need to be made available to part-time instructors.

Delimitations and limitations. One limitation of this study is that the fidel-ity of instructors’ perceptions and the extent of their use of particular technology applications were not measured. In addition, the percentage of full-time instruc-tors was higher than the reported percentage of full-time faculty across several states, particularly at public two-year institutions (see Gerlaugh et al., 2007). A higher representation of full-time faculty than that of the population probably is represented in this study, and therefore, caution should be warranted about gen-eralizing the results. Another limitation is that survey response rates could not be determined because the number of full-time and part-time faculty who taught developmental education at each institution was unknown. Regardless, a contri-bution of this study is that data was collected at the instructor level versus col-lecting data as one representative from the entire institution that has been done in some previous studies (see Parsad et al., 2003; THECB, 2011). There is also the possibility that some colleges and universities are more represented within the sample because a larger percentage of faculty members at a given institution completed the survey. With regard to the DETS instrument, the utilization of more than one tool within each category or the absence of a top choice could not be identified. This represented a limitation with regard to survey design. This research was also subject to the limitations of self-reported data such as those intrinsic errors associated with perceptual recall and bias. Finally, the sample was limited in scope to one state.

Conclusions and ImplicationsDevelopmental mathematics classrooms provide interesting settings to study instructional technology applications because of the varied opportunities and options for applying them. Implications from this study might inform develop-mental education practice and research as to instructors’ top choices of technol-ogy. One implication from this work is the importance of meaningful discourse about the ability to fully scale the integration of technology in a situation where part-time instructors are the primary providers of instruction. Generally, a con-sideration must be whether resources, support, and training for instructional technology applications adequately can be deployed to instructors that might be less than fully engaged on a day-to-day basis with the institution. Specifically, to the state investigated in this work, administrators and policy makers must take into consideration the challenges to effectively implement a mandate that calls for the integration of technology in developmental education classrooms. These challenges include resource disparity across institutions, skill and attitude


differences across instructors with regard to technology, and a predominantly part-time faculty base.

Overall, the study provided empirical evidence that developmental mathe-matics teachers are receptive and somewhat knowledgeable about using instruc-tional technology in the classroom. However, it does not appear that the goal is to utilize technology to promote collaborative learning among students. More research is needed on the integration of technology in developmental mathemat-ics classrooms, particularly with regard to how technology applications might promote the principles and strategies of effective teaching and learning.

ReferencesAmerican Mathematical Association of Two-Year Colleges. (1995, September). Cross-

roads in mathematics: Standards for introductory college mathematics before calculus. Executive summary. Memphis, TN: Author.

American Mathematical Association of Two-Year Colleges. (2006, November). Beyond crossroads: Implementing mathematics standards in the first two years of college. Memphis, TN: Author.

Ashby, J., Sadera, W. A., & McNary, S. W. (2011). Comparing student success between developmental math courses offered online, blended, and face-to-face. Journal of Interactive Online Learning, 10, 128–140.

Bendickson, M. M. (2004). The impact of technology on community college students’ suc-cess in remedial/developmental mathematics (Doctoral dissertation). Retrieved from http://scholarcommons.usf.edu/cgi/viewcontent.cgi?article=1955&context=etd

Berlin, D. F., & White, A. L. (1995). Using technology in assessing integrated science and mathematics learning. Journal of Science Education and Technology, 4, 47–56. doi:10.1007/BF02211581

Blodgett, D. L. (2002). The effects of implementing an interactive student response system in a college algebra classroom. Retrieved from http://www.umaine.edu/center/files/2009/12/BlodgettThesis.pdf.

Bos, B. (2009). Technology with cognitive and mathematical fidelity: What it means for the math classroom. Computers in the Schools, 26, 107–114. doi:10.1080/07380560902906088

Boylan, H.R., & Saxon, D. P. (2012). Attaining excellence in developmental education: Research-based recommendations for administrators. Boone, NC: DevEd Press.

Burrill, G., Allison, J., Breaux, G., Kastberg, S., Leatham, K., & Sanchez, W. (2002). Handheld graphing technology in secondary mathematics: Research findings and implica-tions for classroom practice. Retrieved from http://education.ti.com/sites/UK/downloads/pdf/References/Done/Burrill,G.%2520%282002%29.pdf

Caldwell, J. E. (2007). Clickers in the large classroom: Current research and best-practice tips. Life Sciences Education, 6, 9–20. doi:10.1187/cbe.06–12–0205

Center for Teaching. (2012). Classroom response system (“clickers”) bibliography, Nash-ville, TN, Vanderbilt University. Retrieved from http://cft.vanderbilt.edu/docs/classroom-response-system-clickers-bibliography/#math


Cooper, A. (2012). Today’s technologies enhance wiring in mathematics. The Clear-ing House: A Journal of Educational Strategies, Issues and Ideas, 85(2), 80–85. doi:10.1080/00098655.2011.624394

Duka, M. (2009).The effect of incorporating a technology-based component into the develop-mental mathematics course on the success of students enrolled in MAT 0213. Available from ProQuest Dissertations and Theses database. (UMI No. 1463990)

Ellington, A. J. (2003). A meta-analysis of the effects of calculators on students’ achievement and attitude levels in precollege mathematics classes. Journal for Research in Mathematics Education, 34, 433–463. doi.10.2307/30034795

Ellington, A. J. (2006). The effects of non-CAS graphing calculators on student achievement and attitude levels in mathematics: A meta-analysis. School Science and Mathematics, 106, 16–26. doi:10.1111/j.1949-8594.2006.tb18067.x

Epper, R. M., & Baker, E. D. (2009, January). Technology solutions for developmental math: An overview of current and emerging practices. Retrieved from http://www.gatesfoundation.org/learning/Documents/technology-solutions-for-develop-mental-math-jan-2009.pdf.

Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration? Educational Technology Research and Development, 53(4), 25–39. doi:10.1007/BF02504683

Ertmer, P. A., Ottenbreit-Leftwich, A. T., Olgun, S., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relation-ship. Computers & Education, 59, 423–435. doi:10.1016/j.compedu.2012.02.001

Gavitt, K. A. (2010). A study of developmental algebra and computer aided instruction: Does computer-aided instruction increase the final exam grades of developmental algebra students? Available from ProQuest Dissertations and Theses database. (UMI No. 3397446)

Gerlaugh, K., Thompson, L., Boylan, H., & Davis, H. (2007). National study of developmental education II: Baseline data for community colleges. Research in Developmental Education, 20(4), 1–4.

Heller, J. I., Curtis, D. A., Jaffe, R., & Verboncoeur, C. J. (2005). Impact of handheld graphing calculator use on student achievement in algebra I. Retrieved from ERIC database. (ED493688)

Hew, K. F., & Brush, T. (2007). Integrating technology into k–12 teaching and learning: Current knowledge gaps and recommendations for future research. Educational Technology Research and Development, 55, 223–252. doi.10.1007/s1 1423-006-9022-5

Hollar, J. C., & Norwood, S. (1999). The effects of a graphing-approach interme-diate algebra curriculum on students’ understanding of function. Journal for Research in Mathematics Education, 30, 220–226. doi:10.2307/749612

King, S. O., & Robinson, C. L. (2009). Staff perspectives on the use of technology for enabling formative assessment and automated student feedback. Innovation in Teaching and Learning in Information and Computer Sciences, 8(2), 24–35. doi: 10.11120/ital.2009.08020024


Kinney, D. P., & Robertson, D. F. (2003). Technology makes possible new models for delivering developmental mathematics. Mathematics and Computer Education, 37, 315–328.

Laughbaum, E. D. (2002). Graphing technology—Tool of choice for teaching devel-opmental mathematics. The AMATYC Review, 24, 41–52.

Mathematical Association of America. (2003). Guidelines for programs and departments in undergraduate mathematical sciences. Washington, DC: Author.

Myers, S. A., Martin, M. M., & Mottet, T. P. (2002). Students’ motives for com-municating with their instructors: Considering instructor socio-communicative style, student socio-communicate orientation, and student gender. Communica-tion Education, 51, 121–133. doi:10.1080/03634520216511

MyMathLabTM. [Computer Software]. Upper Saddle River, NJ: Pearson Education.

National Center for Academic Transformation. (2012). Project descriptions sorted by discipline. Retrieved from http://www.thencat.org/PCR/Proj_Discipline.html

National Council of Teachers of Mathematics. (1989). Curriculum and Evaluation Standards for School Mathematics. Reston, VA: Author.

National Council of Teachers of Mathematics. (2000). Principles and standards for school mathematics. Reston, VA: Author.

Ozel, S., Yetkiner, Z. E., & Capraro, R. M. (2008). Technology in K–12 mathematics classrooms. School Science and Mathematics, 108, 80–85. doi:10.1111/j.1949-8594.2008.tb17807.x

Parsad, B., Lewis, L., & Greene, B. (2003). Remedial education at degree-granting postsecondary institutions in fall 2000: Statistical analysis report. Washington, DC: National Center for Education Statistics.

Pearson, A. F. (2010). Real problems, virtual solutions: Engaging students online. Teaching Sociology, 38, 207–214. doi:10.1177/0032055X10370115

Radford, A. W., Pearson, J., Ho, P., Chambers, E., & Ferlazzo, D. (2012, April). Remedial coursework in postsecondary education: The students, their outcomes, and strat-egies for improvement. Berkeley, CA: MPR Associates, Inc. Retrieved from ERIC database. (ED537852)

Rutschow, E. Z., & Schneider, E. (2011, June). Unlocking the gate. What we know about improving developmental education. Retrieved from http://www.mdrc.org/publications/601/full.pdf

Shaw, S. T. (2009). Influence of insturctional technology on learning and persistence of tribal college students: A quasi-experimental study. Available from Pro-Quest Disserta-tions and Theses database. (UMI No. 3375974)

Shore, M. A. (1999). The effect of graphing calculators on college students’ ability to solve procedural and conceptual problems in developmental algebra. Available from Pro-Quest Dissertations and Theses database. (UMI No. 9991773)

Shults, C. (2001). Developmental education: Practices and policies in community colleges (Executive summary, Research Brief AACC-RB-00-2). Washington, DC: Ameri-can Association of Community Colleges.


Skidmore, S. T., Saxon, D. P., Zientek, L. R., & Edmonson, S. L. (2012). Technology integration in developmental education in Texas. (Contract No. 07272). Austin, TX: Texas Higher Education Coordinating Board.

Spradlin, K. D. (2009). The effectiveness of computer-assisted instruction in developmental mathematics. Available from ProQuest Dissertations and Theses database. (UMI No. 3372144)

Taylor, J. M. (2008). The effects of a computerized algebra program on mathematics achievement of college and university freshmen enrolled in a developmental mathematics course. Journal of College Reading and Learning, 39(1), 35–53.

Texas Higher Education Coordinating Board. (2011). Developmental education pro-gram survey (DEPS). Retrieved from http://www.txhighereddata.org/interactive/DEPS.cfm

Vandal, B. (2011). Getting past go. Modular/emporium model. Retrieved from http://gettingpastgo.org/blog/2011/03/22/modularemporium-model/

Weems, G. H. (2002). Comparison of beginning algebra taught onsite versus online. Journal of Developmental Education, 26(1), 10–18.

Young, S., Kelsey, D., & Lancaster, A. (2011). Predicted outcome value of e-mail communication: Factors that foster professional relational development be-tween students and teachers. Communication Education, 60, 371–388.

Zavarella, C. A., & Ignash, J. M. (2009). Instructional delivery in developmental mathematics: Impact on retention. Journal of Developmental Education, 32(3), 2–13.

Zientek, L. R., Schneider, C. L., & Onwuegbuzie, A. J. (2014). Instructors’ percep-tions about student success and placement in developmental mathematics courses. The Community College Enterprise: A Journal of Research & Practice, 20(1), 67–84.


AppendixDETS Questions Analyzed

6.1 In your opinion, to what extent do you prioritize incorporating technology in the developmental education classroom?

NA 1 not a priority .......2....... .......3....... .......4....... 5 an essential priority

8.1 My familiarity with applications and uses of technology in the learning environment is:

1 extremely familiar .......2....... .......3....... .......4....... 5 not at all familiar

9.1 To what extent do institutional policies require or not require the use of technology in the developmental classrooms?

Require 1.......2....... .......3....... .......4....... 5 Not Require

11.1 To what extent do division/departmental policies require or not require the use of technology in the developmental classrooms?

Require 1.......2....... .......3....... .......4....... 5 Not Require

20.1 I use instructional technology in my developmental education classroom.

Yes No

For each category, please identify your top choice as the most important in-structional technology tool in your Developmental Mathematics courses.

27. Hardware and commonly used software

NAClickers (rapid response system)Document reader (ELMO)ExcelGraphing calculatorInteractive whiteboard (SmartBoard)iPad/Tablets/eReadersNon-graphing calculatorPencasts

28. Content-delivery tools

NACommercialized content-based instructional software productsCourse management software (e.g., Blackboard, Angel)Instructor-generated technology-based supplementsOnline Lecture Notes (PowerPoint, Prezi)Podcasts/vodcasts/webcasts


29. Collaborative tools

NABlogsDiscussion boardsSocial media (Facebook, twitter)Videoconferencing (Skype, Elluminate Live, Tegrity, etc.)WikiWord

30. Tools to facilitate communication between instructors and students

NAEarly intervention (e.g., technology-based warning systems such as Starfish)E-mails about course communications (course progress, course feedback, etc.)E-mails about services (tutoring, special events, etc.)Online grade/performance updates

31. Online resources

NAGoogle or other search enginesOnline tutoringOnline chats or online labsWebsites (e.g., Youtube.com)Wolfram Alpha

Footnote

This study was supported by the Texas Higher Education Coordinating Board (Contract Number 07272) and was part of a larger project designed to explore the use of technology in developmental education programs.

Copyright of The Community College Enterprise is the property of Schoolcraft College, and its content may not be copied or emailed to multiple sites or posted on a listserv without the copyright holder’s express written permission. However, users may print,

download, or email articles for individual use.

Date post:	22-May-2020
Category:	Documents
Upload:	others
View:	3 times
Download:	0 times