Data mining for providing a personalized learning path in creativity: An application of decision trees

Customizing a learning environment to optimize personal learning has recently become a popular trend in e-learning. Because creativity has become an essential skill in the current e-learning epoch, this study aims to develop a personalized creativity learning system (PCLS) that is based on the data mining technique of decision trees to provide personalized learning paths for optimizing the performance of creativity. The PCLS includes a series of creativity tasks as well as a questionnaire regarding several key variables. Ninety-two college students were included in this study to examine the effectiveness of the PCLS. The experimental results show that, when the learning path suggested by a hybrid decision tree is employed, the learners have a 90% probability of obtaining an above-average creativity score, which suggests that the employed data mining technique can be a good vehicle for providing adaptive learning that is related to creativity. Moreover, the findings in this study shed light on what components should be accounted for when designing a personalized creativity learning system as well as how to integrate personalized learning and game-based learning into a creative learning program to maximize learner motivation and learning effects.     

Exploring the relation between learning style models and preferred multimedia types

There are many adaptive learning systems that adapt learning materials to student properties, preferences, and activities. This study is focused on designing such a learning system by relating combinations of different learning styles to preferred types of multimedia materials. We explore a decision model aimed at proposing learning material of an appropriate multimedia type. This study includes 272 student participants. The resulting decision model shows that students prefer well-structured learning texts with color discrimination, and that the hemispheric learning style model is the most important criterion in deciding student preferences for different multimedia learning materials. To provide a more accurate and reliable model for recommending different multimedia types more learning style models must be combined. Kolb's classification and the VAK classification allow us to learn if students prefer an active role in the learning process, and what multimedia type they prefer.     

Student Behavior Modeling for an E-Learning System Offering Personalized Learning Experiences

With the advent of computing and communication technologies, it has become possible for a learner to expand his or her knowledge irrespective of the place and time. Web-based learning promotes active and independent learning. Large scale e-learning platforms revolutionized the concept of studying and it also paved the way for innovative and effective teaching-learning process. This digital learning improves the quality of teaching and also promotes educational equity. However, the challenges in e-learning platforms include dissimilarities in learner’s ability and needs, lack of student motivation towards learning activities and provision for adaptive learning environment. The quality of learning can be enhanced by analyzing the online learner’s behavioral characteristics and their application of intelligent instructional strategy. It is not possible to identify the difficulties faced during the process through evaluation after the completion of e-learning course. It is thus essential for an e-learning system to include component offering adaptive control of learning and maintain user’s interest level. In this research work, a framework is proposed to analyze the behavior of online learners and motivate the students towards the learning process accordingly so as to increase the rate of learner’s objective attainment. Catering to the demands of e-learner, an intelligent model is presented in this study for e-learning system that apply supervised machine learning algorithm. An adaptive e-learning system suits every category of learner, improves the learner’s performance and paves way for offering personalized learning experiences.     

Virtual reality for collaborative e-learning

In the past, the term e-learning referred to any method of learning that used electronic delivery methods. With the advent of the Internet however, e-learning has evolved and the term is now most commonly used to refer to online courses. A multitude of systems are now available to manage and deliver learning content online. While these have proved popular, they are often single-user learning environments which provide little in the way of interaction or stimulation for the student. As the concept of lifelong learning now becomes a reality and thus more and more people are partaking in online courses, researchers are constantly exploring innovative techniques to motivate online students and enhance the e-learning experience. This article presents our research in this area and the resulting development of CLEV-R, a Collaborative Learning Environment with Virtual Reality. This web-based system uses Virtual Reality (VR) and multimedia and provides communication tools to support collaboration among students. In this article, we describe the features of CLEV-R, its adaptation for mobile devices and present the findings from an initial evaluation.     

Performance measurement of e-learning using student satisfaction analysis

The purpose of this paper is to analyze e-learning system quality through the analysis of student satisfaction and the usage of learning materials. This analysis takes into consideration both online and traditional students who are using the same e-learning system. The goal of the analysis is to identify factors which influence student satisfaction and to address heterogeneous styles and needs of both groups of students, so that future pedagogical and motivational methods in teaching and learning can be appropriately selected, developed and implemented. It was of particular interest to explore student satisfaction with quality of an e-learning system and online study approach. Reasons that may influence opinions of online and traditional students are examined and presented. The results are used to give recommendations for e-learning improvements and to propose the model with 4 groups of dimensions for performance measurement each of which best represents satisfaction of both groups of students.     

The influence of system characteristics on e-learning use

The benefits of an e-learning system will not be maximized unless learners use the system. This study proposed and tested alternative models that seek to explain student intention to use an e-learning system when the system is used as a supplementary learning tool within a traditional class or a stand-alone distance education method. The models integrated determinants from the well-established technology acceptance model as well as system and participant characteristics cited in the research literature. Following a demonstration and use phase of the e-learning system, data were collected from 259 college students. Structural equation modeling provided better support for a model that hypothesized stronger effects of system characteristics on e-learning system use. Implications for both researchers and practitioners are discussed.     

Face-to-face,blended, flipped,or online learning environment? Impact on learning performance and student cognitions

This study compares four learning environments: face-to-face learning (F2F), fully e-learning (EL), blended learning (BL), and flipped classroom (FC) with respect to students' learning performance. Moreover, this present research studies changes in perceived flexibility, intrinsic motivation, self-efficacy beliefs of students, and the interaction effects in these student variables on learning performance. Two learning environment design elements: (1) lectures (2) group discussions building on guiding questions, were manipulated to create the four learning environments. Third-year undergraduate students (n = 106), enrolled in the “Animal and Human Physiology” course at CanTho University (Vietnam), were randomly assigned to one of the four learning environments. The results suggest a significant positive differential effect on learning performance when studying in a FC and BL setting. No significant interaction effects could be observed regarding changes in perceived flexibility, intrinsic motivation and self-efficacy. However, significant differences between learning conditions were observed in perceived flexibility. Analysis of focus group data corroborate the finding that students experience more flexibility in time and place when studying in FC, BL and EL environments. In addition, students in a FC environment reflect significantly larger positive changes in their self-efficacy. But, the qualitative data show how positive perceptions about flexibility, motivation and self-efficacy are often cancelled out by negative perceptions.     

Exploring feedback and student characteristics relevant for personalizing feedback strategies

Personalized tutoring feedback is a powerful method that expert human tutors apply when helping students to optimize their learning. Thus, research on tutoring feedback strategies tailoring feedback according to important factors of the learning process has been recognized as a promising issue in the field of computer-based adaptive educational technologies. Our paper seeks to contribute to this area of research by addressing the following aspects: First, to investigate how students' gender, prior knowledge, and motivational characteristics relate to learning outcomes (knowledge gain and changes in motivation). Second, to investigate the impact of these student characteristics on how tutoring feedback strategies varying in content (procedural vs. conceptual) and specificity (concise hints vs. elaborated explanations) of tutoring feedback messages affect students' learning and motivation. Third, to explore the influence of the feedback parameters and student characteristics on students' immediate post-feedback behaviour (skipping vs. trying to accomplish a task, and failing vs. succeeding in providing a correct answer). To address these issues, detailed log-file analyses of an experimental study have been conducted. In this study, 124 sixth and seventh graders have been exposed to various tutoring feedback strategies while working on multi-trial error correction tasks in the domain of fraction arithmetic. The web-based intelligent learning environment ActiveMath was used to present the fraction tasks and trace students' progress and activities. The results reveal that gender is an important factor for feedback efficiency: Male students achieve significantly lower knowledge gains than female students under all tutoring feedback conditions (particularly, under feedback strategies starting with a conceptual hint). Moreover, perceived competence declines from pre- to post-test significantly more for boys than for girls. Yet, the decline in perceived competence is not accompanied by a decline in intrinsic motivation, which, instead, increases significantly from pre- to post-test. With regard to the post-feedback behaviour, the results indicate that students skip further attempts more frequently after conceptual than after procedural feedback messages.     

Can structured representation enhance students' thinking skills for better understanding of E-learning content?

This paper proposes an e-learning model to assess the effects of online structured representation of content on learners' understanding. We designed a structured representation based on the theory of distributed cognition that provides seven segments for reading research articles. The study hypothesized that motivation, attention, and interactivity are essential factors that affect students' thinking skills for understanding e-learning content. To investigate and confirm the effect of these factors on the students' thinking skills, we designed a survey and analyzed the responses of 210 university students concerning the proposed structured representation. The results revealed that motivation, attention, and interactivity did contribute to the students' thinking skills. They also demonstrated that the structured representation helped students achieve an adequate level of thinking skills as they read research articles, which had a positive effect on their understanding. This finding demonstrates that structured representation has significant potential as a learning tool and that structure-based e-learning can influence students' metacognitive activities and facilitate their understanding.     

Integration into mathematics classrooms of an adaptive and intelligent individualized e-learning environment: Implementation and evaluation of UZWEBMAT

The purpose of this study is to design an adaptive and intelligent individualized e-learning environment based on learning style and expert system named UZWEBMAT and to evaluate its effects on students’ learning of the unit of probability. In the study, initially, learning objects were prepared in three different ways in relation to Visual–Auditory–Kinesthetic (VAK) learning style for each subject of the probability unit. These were appropriate for secondary school mathematics curricula. Then, they were transferred into the digital environment. Each student may follow a different course, and the solution supports s/he will get may also differ highlighting the individual learning. The sample of the study consists of 81 10th grade students from two high schools in Trabzon, Turkey. Qualitative and quantitative data were collected from students to answer research questions. Quantitative data were given as frequency distribution and percentages. Qualitative data were analyzed using qualitative data analysis methods. Results of the study indicated that opinions regarding UZWEBMAT are rather positive. Aiming at individual learning, UZWEBMAT provides the most appropriate environment for students. In addition, UZWEBMAT can be used as well to reinforce traditional classroom education.     

Analysis of learners' navigational behaviour and their learning styles in an online course

Providing adaptive features and personalized support by considering students' learning styles in computer‐assisted learning systems has high potential in making learning easier for students in terms of reducing their efforts or increasing their performance. In this study, the navigational behaviour of students in an online course within a learning management system was investigated, looking at how students with different learning styles prefer to use and learn in such a course. As a result, several differences in the students' navigation patterns were identified. These findings have several implications for improving adaptivity. First, they showed that students with different learning styles use different strategies to learn and navigate through the course, which can be seen as another argument for providing adaptivity. Second, the findings provided information for extending the adaptive functionality in typical learning management systems. Third, the information about differences in navigational behaviour can contribute towards automatic detection of learning styles, helping in making student modeling approaches more accurate.     

When educational agents meet surrogate competition: Impacts of competitive educational agents on students' motivation and performance

The design of educational agents increasingly attracts researchers' attention recently. One of major reasons is that educational agents could enhance student learning in various aspects. Although research in this area has mushroomed, such research mainly emphasizes on students in higher education. It is still unclear how educational agents influence young student learning. In addition, competition is a significant element, but fewer studies take competition into account while designing educational agents. Although some studies have indicated that educational agents in competitive environments has positive effects on students' perception and attribution, its impacts on students' motivation and performance are unclear. Thus, the study develops an integrative agent that combines educational and competitive elements for young students, and further examines its influences, in terms of motivation and learning performance. The results revealed that such competitive educational agents could enhance students' motivation and learning performance.     

Exploring the role of e-learning readiness on student satisfaction and motivation in flipped classroom

Decrease in student satisfaction and motivation in flipped classroom (FC) model of instruction is among frequently observed problems. And these problems could reduce the efficiency of FC model. It is believed that the problems related to student satisfaction and motivation in FC model of instruction are related to the e-learning readiness of the students. The purpose of the current study was to explore the impact of the e-learning readiness of the students on student satisfaction and motivation in FC model of instruction. The study was carried out with 236 undergraduate students taking Computing I class taught using FC model of instruction. Data were collected from three self-report instruments: E-learning Readiness Scale (subscales: ‘computer self-efficacy’, ‘internet self-efficacy’, ‘online communication self-efficacy’, ‘self-directed learning’, ‘learner control’ and ‘motivation towards e-learning’), Satisfaction Scale and Motivated Strategies for Learning Questionnaire. The path analyses with structural equation modelling (SEM) further verified that students' e-learning readiness were related to their satisfaction and motivation while undertaking academic tasks in FC model of instruction. The results of the study indicated that students' e-learning readiness was a significant predictor of their satisfaction and motivation in FC model of instruction. Findings were presented and discussed for future studies and applications.     

Enhancement of student learning performance using personalized diagnosis and remedial learning system

Although conventional student assessments are extremely convenient for calculating student scores, they do not conceptualize how students organize their knowledge. Therefore, teachers and students rarely understand how to improve their future learning progress. The limitations of conventional testing methods indicate the importance of accurately assessing and representing student knowledge structures. The personalized diagnosis and remedial learning system (PDRLS) proposed in this study enhances the effectiveness of the Pathfinder network by providing remedial learning paths for individual learners based on their knowledge structure. The sample was 145 students enrolled in introductory JAVA programming language courses at a Central Taiwan technology university. The experimental results demonstrate that learners who received personalized remedial learning guidance via PDRLS achieved improved learning performance, self-efficacy, and PDRLS use intention. The experimental results also indicated that students with lower knowledge level gain more benefits from the PDRLS than those with higher level of knowledge and that field dependence (FD) students obtain a greater benefit from PDRLS than field independence (FI) students do.     

The role of feedback and self-efficacy on web-based learning: The social cognitive perspective

The social cognitive perspective of self-regulated learning suggests that effective learning is determined by the interactions among personal, behavioral, and environmental influences; particularly, high self-regulated learners hold higher motivation (personal), apply better learning strategies (behavioral) and respond to environmental demand more appropriately (environmental). The study thus uses the social cognitive perspective to explore the role of self-efficacy (personal), student feedback behavior, use of learning strategies (behavioral), performance and receiving feedback (environmental) in Web-based learning. There were 76 university students participated in this study. Both quantitative and qualitative methods were applied for data analysis. The results supported that self-efficacy predicted student use of learning strategies and related to elaborated feedback behavior (personal → behavioral). High self-efficacy students applied more high-level learning strategies, such as elaborative strategy and critical thinking. Students who provided elaborated feedback also had higher self-efficacy than those who did not. Moreover, receiving elaborative feedback significantly promoted student self-efficacy (environmental → personal), while receiving knowledge of correct response improved student performance. However, the results indicated that feedback behaviors did not predict academic performance, which may be interfered by modeling effects.     

A distributed system for learning programming on-line

Several Web-based on-line judges or on-line programming trainers have been developed in order to allow students to train their programming skills. However, their pedagogical functionalities in the learning of programming have not been clearly defined. EduJudge is a project which aims to integrate the “UVA On-line Judge”, an existing on-line programming trainer with an important number of problems and users, into an effective educational environment consisting of the e-learning platform Moodle and the competitive learning tool QUESTOURnament. The result is the EduJudge system which allows teachers to apply different pedagogical approaches using a proven e-learning platform, makes problems easy to search through an effective search engine, and provides an automated evaluation of the solutions submitted to these problems. The final objective is to provide new learning strategies to motivate students and present programming as an easy and attractive challenge. EduJudge has been tried and tested in three algorithms and programming courses in three different Engineering degrees. The students’ motivation and satisfaction levels were analysed alongside the effects of the EduJudge system on students’ academic outcomes. Results indicate that both students and teachers found that among other multiple benefits the EduJudge system facilitates the learning process. Furthermore, the experiment also showed an improvement in students’ academic outcomes. It must be noted that the students’ level of satisfaction did not depend on their computer skills or their gender.     

eTeacher: Providing personalized assistance to e-learning students

In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking online courses and automatically builds the student’s profile. This profile comprises the student’s learning style and information about the student’s performance, such as exercises done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks. Then, eTeacher uses the information contained in the student profile to proactively assist the student by suggesting him/her personalized courses of action that will help him/her during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.     

The effectiveness of adaptive difficulty adjustments on students' motivation and learning in an educational computer game

Computer games that adaptively adjust difficulty are used to continuously challenge players according to their abilities. The adjustment of difficulty occurs automatically in response to a game's ongoing assessment of a player's performance. This approach to difficulty adjustment is likely to be of value in educational computer games as a means of scaffolding learning for students. However, there is limited research evaluating the effectiveness of educational computer games with adaptive difficulty adjustment when compared to non-adaptive difficulty adjustment. To expand on this research a quasi-experimental study was designed to isolate the impact of the difficulty adjustment game element on motivation and learning. A total of 234 secondary school students were allocated to one of three activities involving learning about Spanish cognates: an adaptive difficulty adjustment game, an incremental difficulty adjustment game that was non-adaptive, and a written activity. The three learning activities were designed following the same learning and motivation theories. The two games were identical apart from the difficulty adjustment mechanism. The results for motivation indicated that all students experienced high levels and there was no significant difference between the three learning activities. The pre- and post-tests results for learning indicated that significantly higher learning outcomes were achieved by students who played the adaptive game. Analysis of a game log recording the correctness of students' responses indicated that the adaptive difficulty adjustment game, in contrast to the non-adaptive incremental difficulty adjustment game, provided a scaffolding structure to enhance student learning.     

Effects of the heterogeneity of game complexity and user population in learning performance of business simulation games

Previous studies on business simulation games (BSGs) have concluded that improved performance may not be the primary benefit of using BSGs, due to mixed results between student performance and perceptions. Two relevant and insightful issues attract our attention, namely, the impacts of the heterogeneous student population and the different complexity levels of BSG software. To address these issues, the present study aims to understand the relationship between student profile/characteristics and performance in the classroom with BSG-facilitated learning. An in-depth case study is conducted on a general college course designed to teach three different complexities of BSGs to students enrolled in different majors. Four student profile factors are individually tested for differences in performance scores as evaluated by the teacher. Additionally, the influences of 11 student characteristics are assessed with regard their self-reported perceived learning performances. Regression analysis and ANOVA are used to investigate the impacts of heterogeneous users and game complexity on student performance. Based on the regression analyses of the data collected from 43 respondents who participated in the general course, the study concludes that knowledge and skill may influence the heterogeneous student population; moreover, student participation and tacit learning preference improve performance, and students with an auditory learning preference or high learning motivation may not perform well in classroom BSG learning. However, the low value of adjusted R square implies that more dimensions or variables are needed to increase the explaining power of the performance scores in the regression analyses. In contrast, heterogeneous BSG software with different complexity levels present different results. The current research contributes practical and incremental knowledge on the complexity of heterogeneous BSG software on performance scores and the perceived learning performance of heterogeneous student populations. With the research limitations acknowledged, a series of suggestions for teachers pertaining to appropriate applications of BSGs in classes is offered as well as recommendations to BSG providers. Nevertheless, in-depth analyses are required, preferably with larger student population samples, to further explore the insignificant relationship between student perceptions and attitude under nonlinear extended complexity.     

Effects of Collaborative Web Based Vocational Education and Training (VET) on Learning Outcomes

Considering the increase in need of skilled human capital, the vocational training and education through e-learning has widely been adopted all over the world. It serves as a major alternative for training and teaching professionals, technicians, skilled workers, amateurs and students who cannot manage regular college education due to time, cost and distance factors. Prior research focuses on learning enhancement from several perspectives like computer mediated methods, learning games, personalized learning environments and web based instructional design. The proposed work contributes to this debate by investigating the role of collaboration i.e. student–student and student–instructor interaction, support material, instructor's feedback and evaluating their effects on learning outcome and students' learning experience. The investigation is performed through a case study presenting four vocational courses offered in three consecutive sessions. The findings revealed that collaborative practices i.e. group work, team effort, in time instructor's feedback and consolidated support material enhances learning experience of student and contributes positively to the learning outcome.