Development of an Autonomous, Intelligent and

Adaptive E-learning System

Boštjan Šumak*, Vili Podgorelec*, Sašo Karakatič*, Kosta Dolenc**, Andrej Šorgo** * University of Maribor, Faculty of Electrical Engineering and Computer Science, Koroška cesta 46, 2000 Maribor,

Slovenia ** University of Maribor, Faculty of Natural Sciences and Mathematics, Koroška cesta 160, 2000 Maribor, Slovenia

E-mail: {bostjan.sumak, vili.podgorelec, saso.karakatic, kosta.dolenc, andrej.sorgo}@um.si

Abstract - Autonomous Intelligent and Adaptive E-

learning Systems (AIAESs) are a generation of learning

systems that include the individuality and personalization of

the student in the learning process, similar to what happens

in a traditional individualized lesson with one teacher and

one student. This paper reports the design and development

architecture of an AIAES, the main objective of which is to

improve the information literacy of adolescents. Computer

systems such as AIAES need to provide the same or at least

similar, instructional and interactional advantages as those

found in the traditional human tutoring process, which has

proven successful, and has represented the most efficient

method of learning and teaching. Development of an AIAES,

therefore, implies an interdisciplinary approach that

connects various fields, including Computer Science,

Cognitive Science, Artificial Intelligence, and Functional

Literacy to fields connected to education. The developed

services for intelligent analysis of various metadata provide

the AIAES with the ability to adapt the learning course based

on an individual learner’s learning abilities in the learning

process.

Keywords – e-learning systems; Intelligent and Adaptive E-

learning Systems;

I. INTRODUCTION

Terms such as digital and information literacy are used to describe the current and future knowledge and skills required to enable an individual to use Information and Communication Technologies (ICT) efficiently. Digital Literacy (DL) is “the awareness, attitude and ability of individuals to use digital tools and facilities appropriately to identify, access, manage, integrate, evaluate, analyze and synthesize digital resources, construct new knowledge, create media expressions, and communicate with others in the context of specific life situations, in order to enable constructive social action” [1]. Information Literacy (IL) is defined as “a set of abilities that allow individuals to recognize when information is needed, and to locate the required information, evaluate it and use it effectively.” [2]. IL can also be defined as “a set of skills, attitudes and knowledge necessary to know when information is needed to help solve a problem or make a decision, how to articulate that information need in searchable terms and language, then search efficiently for the information, retrieve it, interpret and understand it, organize it, evaluate its credibility and authenticity, assess its relevance, communicate it to others if necessary, then utilize it to accomplish bottom-line purposes” [3].

Today, just using contemporary ICT for surfing, social networking and communication are not enough. An

information literate individual must be able to learn how to utilize different ICTs efficiently in order to search for, retrieve, organize, analyze, evaluate information, and then use it for everyday problem-solving tasks. An information literate person must be able to (1) Determine the extent of information needed, (2) Access the required information effectively and efficiently, (3) Evaluate information and its sources critically, and incorporate selected information into their knowledge base, (4) Use information effectively to accomplish a specific purpose, and (5) Understand the economic, legal and social issues surrounding the use of information, as well as access and use information ethically and legally.

The term digital natives tags a generation born during or after the introduction of ICT into daily routines. Digital natives’ skills are frequently believed to be overlapping with IL skills and competencies. However, research shows that ICT experiences do not necessarily contribute to Information Literacy [4]. Moreover, several recently published studies show that IL is still below acceptable levels. For example, Stanford researchers found that students have trouble judging the credibility of online information [5]. The International Computer and Information Literacy Study revealed that, among other findings, many “digital natives” are not digitally competent, and that being born in the digital era is not a sufficient condition for being able to use ICT in a critical, creative and informative way [6]. The study [6] also revealed that, on average, girls outperformed boys in computer and information literacy, and that teachers who used ICT in their classes not only taught more effectively but also developed a more transversal computer and information literacy in their students.

IL can be regarded as both work-specific and generic and is a prerequisite for the fulfilment of the demands of lifelong learning in the future workplace, as well as for the realization of personal choices. In the existing literature, we have not been able to identify an existing e-learning system integrating services for identification (diagnosis) of IL levels at the secondary school level [7] and services for the improvement of IL of adolescents. Recently AI systems found their way in education as an association of intelligence with machines, which can think strategically [8]. However, search in peer reviewed journals shows that applications of AI in education are now in an early phase, and without clear answers and triangulated solutions, especially with association with the rising of importance of social networks in education which “focus mainly on the incorporation of social features and do not yet provide

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personalized and adaptive tutoring (p.1)”. In order to raise interest in solutions provided by the application of AI [9], our approach to connect AI with Information Literacy can be regarded as promising.

The main purpose of this work is to design, develop and establish an Autonomous, Intelligent and Adaptive E-learning System (AIAES). Such intelligent IL e-learning environment should provide advanced services enabling its application as a self-educational tool, or be included in the school-based educational process. The architecture of the system will allow self-assessment based on descriptors as listed in the work of Dolničar et al. [10], assigned formative tasks and final assessment of learning outcomes. All functionalities of the system are planned to be able to adapt to a student’s previous learning experience, individual progress and tempo, cognitive level, and personal preferences. At the recent stage of design of the AI, specific research questions and follow up hypotheses are foreseen. However, they are not yet operationalized in a testable format. In generic form, the questions are going to be formalized around the ideas of effects on learning outcomes, suitability, applicability, and acceptability of AI in teaching/learning contexts, and a number of personal factors affecting it.

II. AUTONOMOUS, INTELLIGENT AND ADAPTIVE E-

LEARNING SYSTEMS

The traditional human tutoring process has proven successful, and has represented the most efficient method of learning and teaching since the beginning of teaching. However, teaching and learning processes are changing, and have become flexible and adaptive [11]. In existing literature, researchers mostly agree that new learning technologies have the potential to transform education and training. Recent developments of modern technologies and strong collaboration of scientists from different fields (e.g. Cognitive Science, Computer Science, Artificial Intelligence, Functional Literacy and fields connected to education) have contributed significantly to the development of advanced e-learning systems. By use of such a system, an individual can learn in different ways, mainly through self-learning and learning supported by various formal and informal educators. E-learning systems support e-learning when they provide learning contents, rules to guide learning and achievement thresholds, technologies to enhance learning, a learning environment to make students engaged in learning, and learning platforms and tools to serve learning [11].

Advancements in digital technologies and intelligent learning environments impact developments of contemporary e-learning environments significantly [12]. Autonomous, Intelligent and Adaptive E-learning Systems (AIAES) are the next generation of e-learning systems that model and understand the individuality and personalization of the learner in the learning process efficiently, similar to what happens in a traditional individualized lesson with one teacher and one student. AIAES are powerful adaptive educational systems providing services and modules for personalized learning for students with different backgrounds, abilities, behaviors and knowledge. The personalization in an e-learning system can be achieved by implementing different techniques, such as intelligent

agents (e.g. [13], [14], [15]), tag-based recommendation systems (e.g. [16], [17]), data mining algorithms [18], and others.

Such systems are based on Artificial Intelligence for providing individualized instructions based on a learner’s characteristics, learning habits and preferences so that each individual learner gets learning contents and instructions that best meet his/her personal needs. AIAES is able to provide an immediate and efficient solution to a student’s learning problems, and help the student to achieve maximum learning gain. Within AIAES, the content is in accordance with the cognitive learning approach, and the system provides services for intelligent analysis (collection of various variables and metadata), which enables Machine Learning and the ability to adapt to the learning course based on the learner’s individual characteristics and abilities in the learning process. Such system will overcome weaknesses of self-assessment instruments for assessment of IL as proposed by [19].

An AIAES’ architecture must contain the following modules: (1) Student module (who to teach), (2) Domain module (what to teach), (3) Instructor module (how to teach), and (4) Interface module (the interaction environment). The student module is the basis for making an AIAES adaptive, and is responsible for managing the cognitive state through creating the student profile that includes information such as the student’s personal data, learning preference, current knowledge state, etc. The domain module should provide an interesting environment that allows replacement of obsolete topics and tasks, and the addition of topics and tasks which best suits different target groups of learners. The instructor module is responsible for making instructional decisions related to the pedagogical aspects of learning such as (1) Correct choice of the teaching methods and learning materials that best suit the individual student’s profile, (2) Assessing the cognitive state of individual learner, (3) Deciding whether the student is able to proceed to the next learning stage in learning, etc. The interface must provide a fully adaptable interaction environment based on the user’s role preferences (e.g. a device, which can be a desktop computer, laptop, tablet PC, smartphone, etc.). Based on the prepared learning content and collected metadata about the learners’ status, an information dashboard should be designed to provide important data aggregations in the form of visual analytics.

III. DEVELOPMENT OF AN AIAES TO SUPPORT

LEARNING OF INFORMATION LITERACY

In the project Development, testing and validation of an Autonomous Intelligent and Adaptive E-learning System for the improvement of Information Literacy of adolescents [20], the Information Literacy was modeled with seven standards and corresponding performance indicators [10] (see Figure 2. s). The standards were adapted loosely from a reference list of the DigComp initiative [21]. The main difference was that IL was put in a wider context with the addition of Standards (e.g. safety) originally not considered as IL. For each performance indicator, several learning outcomes on different proficiency levels (basic, intermediate and advanced) were specified, resulting in almost 100 learning outcomes.

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Figure 1. User types in the AIAES system

The design and development of the AIAES includes the development of modules, or functionalities, for different user types (Figure 1. provides a high overview of functionalities for different user types: Learner, instructor and administrator). A learner uses the AIAES for (1) Assessment of existing knowledge (2) Formative learning by consuming learning materials and performing learning tasks (3) Summative assessment of learning outcomes. Therefore, the AIAES must provide intelligent assessment services and immediate feedback at each progressive step,

which are the basis for implementing an adaptive learning process. Individualized paths enable a learner to consume learning materials in line with learning capacity, individual preferences, cognitive level and knowledge. Instructors are allowed to use the AIAES in several optional but not exclusive ways. Because its major intention is to allow individualization in a process of learning, it was not designed for traditional teacher-centered lessons, even if it can be used for such purpose. The first option is to be used individually by the students, with teachers as a background and providers of help in need. The second option is the application of AIAES for assignment of homework or learning materials during the lessons. In order to provide better learner’s performance supervision, the AIAES must also provide several intelligent services for monitoring and guiding the learner in the learning process (e.g. initial analysis and assessment of the learner’s cognitive abilities and knowledge, supervision during the learner’s studying process, collecting of metadata during the supervision, alerting and advising the student, etc.). Additionally, instructors need a dashboard, which displays all important information relevant to a learners’ performance. The last is intended to be used as a template for preparing their own learning materials, contents and learning tasks to be used by the learners in their classrooms in a traditional way.

The AIAES System

Learner

Instructor

Administrator

Learner s set of functionalities (use cases)

Instructor s set of functionalities (use cases)

Administrator s set of functionalities (use cases)

Figure 2. The proposed Information Literacy Schema

Information

Literacy

1. Ability to

determine the

information needs

2. Ability to find

information

3. Ability to assess

information

5. Ability to

integrate and use

information

6. Ability to use

information

ethically

7. Ability to use

information in a

safe way

1.1. Specify the information need

1.2 Identify sources of information (depending on

purpose, scope and suitability)

1.3 Select/use different sources

1.4 Formulate search intention

2.1 Select methods and tools for search

2.2 Develop and implement a search (in the general

and specialized search engines)

3.1 Can use criteria for evaluation of information,

sources and procedures for their collection

3.2. Determine the usefulness of the information

4.1 Able to store and organize sources

4.2 Able to use tools for collaboration,

communication and information sharing

5.1 Able to refer to sources (citing sources) in the

text and in list of references

5.2 Able to summarize the content

5.3 Can read and understand information from the

graphic presentations

5.4 Can display information with appropriate

graphical tools

6.1 Uses information ethically

6.2 Masters online bonton

6.3Takes into account the legal aspects of the use of

information

4. Ability to

manage

information

7.1 Recognizes and takes into account security

threats to hardware and software

7.2 Protects personal and financial information

7.3 Able to identify personal risk

Standard Performance Indicator

Learning Outcomes

Proficiency

Levels: AdvancedBasic

Inter-

mediate

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They are invited to communicate their tasks to the administrators to be included in the system. Administrators are responsible for managing users, basic system administration, including the administration of some basic entities, such as the definitions for proficiency levels, Standards, performance indicators, and learning outcomes.

Based on the proposed IL schema, the main entities for the AIAES were modeled in order to support the management of learning contents, lessons and learning tasks, in order to support an e-learning environment for the IL (see Figure 2. s). Standard entity is on the highest level of the conceptual model and is used to distinguish between seven different competences or dimensions of the IL. Each standard can cover various performance indicators, which in this project were modeled as a Performance indicator.

Figure 2. Abstract conceptual model of main entities in the AIAES¸

For each performance indicator, several learning outcomes (Learning Outcome) on different proficiency

StandardPerformance

Indicator

Learning

Outcome

Proficiency

Level

Learning

Lesson

Learning

Content

Learning Task

(Question)

Additional

Content

Figure 3. Management of a learning task (in the Slovene language)

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levels (in this project, three proficiency levels were specified: basic, intermediate and advanced). For each learning outcome, various learning contents, learning lessons and learning tasks can be specified, where each of these can also be re-used for different learning outcomes. Additional content can be provided for a certain learning lesson and learning task, in order to provide a learner with an additional explanation if the basic content is not enough for comprehension.

For instructors, responsible managing of the learning contents, which includes management of IL Standards, proficiency levels, performance indicators, learning outcomes, learning content, learning lessons, and learning tasks, several management services were implemented (because of the space limit, only two of the management pages are shown in Figure 3. and Figure 4. s).

Figure 4. Management of IL Standards

We wanted the learning process to be optimized for each individual learner (i.e. personalized). This optimization is driven primarily towards the goal of achieving the best possible final outcome (final assessment of learning outcomes) with the least needed interactions between a learner and the system. For example, if a learner can achieve the same (or even better) final result without assigning him/her with a specific formative task, then the process which does not include this specific formative task is preferred. If, on the other hand, including a specific task into a learner’s personalized learning process yields a better final result, then such a process is preferred.

As the personalization needs to be performed dynamically during the implementation of the learning process, the optimization is based on the results of already performed learning processes by other learners. All the data gathered from all already performed learning processes, can help us to determine the best sequence of different learning tasks automatically, which will, with the highest probability, result in the best result for the learner. In this way, the collective intelligence of a set of learners and their assessment results help us discover the common learning patterns of successful, as well as not so successful, learning paths throughout the set of different learning contents, lessons and tasks.

An example of a formalization of such a dynamic learning path, represented in a form of a dynamic decision tree structure, is presented in Figure 6. This example represents a situation where learning content with the code 1.1.1.1 is first presented to a learner on the basic proficiency level, who then proceeds to the question 1.1.1.1. If the answer is correct, then the learner proceeds

to the intermediary proficiency level, and, if not, he/she is provided with more learning content at the same level. Such a decision tree is initially constructed manually in a way that represents a general learning process for an average learner. By gathering more and more data, the tree structure is being optimized towards the most efficient learning process for a specific learner, based on his/her results from given tasks.

Content1.1.1.1 (abs)dummy < 1

Question1.1.1.1 (abs)correct < 1

End--

Lesson1.1.1.1 (abs)dummy < 1

Intermediary-

correct < 3

Lesson0.0.-1.0 (rel)correct < 1

Content0.1.1.0 (rel)correct < 1

Figure 6. Dynamic decision tree structure for dynamic learning path

formalization

As we wanted the developed AIAES to work in an automatic manner, without a need for unnecessary involvement of either the instructor or the administrator, the designed decision tree structure is also very appropriate for the implementation of the described learning process from the technical point of view. Using the pre-determined coding system, it references the specific learning content directly, i.e. lessons and/or tasks. While traversing along the tree structure, the system fetches the required content from the database, and provides it adequately to the learner. In this manner, the personalized and dynamic behavior of the system is achieved without a need to pre-determine a huge set of different learning scenarios.

The optimization of the dynamic learning path is done on several metrics gathered from each individual during their initial performance: The time spent on each exercise, the accuracy of the individual’s answers in the assessment phase, and the individual’s performance along the lessons. Some of the metrics are inputs into the validation phase, which checks if the individual student is actually participating in the process, or is the individual only rushing through the learning path with random answers. In addition, the optimization process takes the length of the learning path into account, so that the learning paths are as short as possible while keeping the content retention rate of the learned topic as high as possible.

IV. CONCLUSION

In the paper, we presented the process of design and the implementation of the Autonomous Intelligent and Adaptive E-learning System (AIAES) on the case of Information Literacy learning. We presented an

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architectural overview of the proposed system, the architecture of the database, the actors that use the system, the flow of the use case of the system, and overview of the methods that take care of the adaptation of the learning paths for each individual student.

There are still numerous open questions and research potential in the learning path optimization subject, where one can apply meta-heuristic methods either just to optimize small parameters (such as time allowed for any particular question or set of questions, number of repetitions), or the whole learning path in general (the order of the questions and the lessons). Beside the meta-heuristic algorithms, data mining can be used here to evaluate the most appropriate questions and the lesson to be used in the learning paths that result in the best results of the students.

Evaluation of the system has not yet been performed, so both approaches need an extensive and wide experiment with the control group (learning paths set by experts – teacher), to validate if adaptive and intelligent learning paths perform better than well-structured human learning paths.

ACKNOWLEDGEMENT

The authors acknowledge the financial support from the Slovenian Research Agency (Research Core Funding No. (J5-8230).

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