Adaptation provisioning with respect to learning styles in a Web‐based educational system: an experimental study

Personalized instruction is seen as a desideratum of today's e‐learning systems. The focus of this paper is on those platforms that use learning styles as personalization criterion called learning style‐based adaptive educational systems. The paper presents an innovative approach based on an integrative set of learning preferences that alleviates some of the limitations of similar systems. The adaptive methods used as well as their implementation in a dedicated system (WELSA) are presented, together with a thorough evaluation of the approach. The results of the experimental study involving 64 undergraduate students show that accommodating learning styles in WELSA has a beneficial effect on the learning process.     

A NEW E‐LEARNING ACHIEVEMENT EVALUATION MODEL BASED ON ROUGH SET AND SIMILARITY FILTER

The trend of utilizing information and Internet technologies as teaching and learning tools is rapidly expanding into education. E‐learning is one of the most popular learning environments in the information era. The Internet enables students to learn without limitations of space and time. Furthermore, the learners can repeatedly review the context of a course without the barrier of distance. Recently, student‐centered instruction has become the primary trend in education, and the e‐learning system, which is considered with regard to of personalization and adaptability, is more and more popular. By means of e‐learning systems, teachers can adjust the learning schedule instantly for each learner according to a student's achievements and build more adaptive learning environments. Sometimes, teachers give biased assessments of students’ achievements under uncontrollable conditions (i.e., tiredness, preference) and are in dire need of overcoming this predicament. To solve the drawback mentioned, a new model to evaluate learning achievements based on rough set and similarity filter is proposed. The proposed model includes four facets: (1) select important features (attributes) to enhance classification performance by feature selection methods; (2) utilize minimal entropy principle approach (MEPA) to fuzzify the quantitative data; (3) select linguistic values for each feature and delete inconsistent data using the similarity threshold (similarity filter); and (4) generate rules based on rough set theory (RST). The practical e‐learning achievement data sets are collected by an e‐learning online examination system from a university in Taiwan. To verify our model, the performances of the proposed model are compared with the listing models. Results of this study demonstrate that the proposed model outperforms the listing models.     

A contextual game-based learning approach to improving students' inquiry-based learning performance in social studies courses

Inquiry-based learning, an effective instructional strategy, can be in the form of a problem or task for triggering student engagement. However, how to situate students in meaningful inquiry activities remains to be settled, especially for social studies courses. In this study, a contextual educational computer game is developed to improve students' learning performance based on an inquiry-based learning strategy. An experiment has been conducted on an elementary school social studies course to evaluate the effects of the proposed approach on the inquiry-based learning performances of students with different learning styles. The experimental results indicate that the proposed approach effectively enhanced the students' learning effects in terms of their learning achievement, learning motivation, satisfaction degree and flow state. Furthermore, it is also found that the proposed approach benefited the “active” learning style students more than the “reflective” style students in terms of learning achievement. This suggests the need to provide additional supports to students with particular learning styles in the future.     

Adaptive course generation through learning styles representation

This paper presents an approach to automatic course generation and student modeling. The method has been developed during the European funded projects Diogene and Intraserv, focused on the construction of an adaptive e-learning platform. The aim of the platform is the automatic generation and personalization of courses, taking into account pedagogical knowledge on the didactic domain as well as statistic information on both the student’s knowledge degree and learning preferences. Pedagogical information is described by means of an innovative methodology suitable for effective and efficient course generation and personalization. Moreover, statistic information can be collected and exploited by the system in order to better describe the student’s preferences and learning performances. Learning material is chosen by the system matching the student’s learning preferences with the learning material type, following a pedagogical approach suggested by Felder and Silverman. The paper discusses how automatic learning material personalization makes it possible to facilitate distance learning access to both able-bodied and disabled people. Results from the Diogene and Intraserv evaluation are reported and discussed.     

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.     

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.     

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.     

Here and now mobile learning: An experimental study on the use of mobile technology

Mobile technology opens the door for a new kind of learning called here and now learning that occurs when learners have access to information anytime and anywhere to perform authentic activities in the context of their learning. The purpose of this study was to investigate the effects of here and now mobile learning on student achievement and attitude. The research questions addressed were (1) Does “Here and Now” mobile learning significantly improve student achievement when compared with Computer based Instruction? (2) Does “Here and Now” mobile learning significantly improve student attitude when compared with Computer based Instruction? (3) Are there differences in student achievement and attitudes when “Here and Now” mobile learning is delivered using a tablet versus ipod? 109 undergraduate students enrolled in preservice instructional design and instructional technology courses at a regional southeastern university participated in the study. Participants took a pretest at the beginning of the study, and then were assigned to one of the versions of an art lesson (CBI version and iPad/iPod version) which were developed using Lectora Inspire incorporating information on five different paintings in the education building. After the lesson, they completed the posttest and an attitude survey. ANOVA was conducted on data obtained from the achievement posttest and on the attitude survey results for the Likert type items (Items 1–12). Analyses on achievement and attitude data revealed positive significant differences. The CBI treatment achieved positive posttest scores on the posttest while the iPad/iPod treatments had positive attitudes. This study has implications for those designing and implementing mobile learning.     

An application of adaptive games-based learning based on learning style to teach SQL

The fact that each student has a different way of learning and processing information has long been recognised by educationalists. In the classroom, the benefits derived from delivering learning content in ways that match the student's learning style have also been identified. As new modes of delivery of learning content such as computer-assisted learning systems (e.g. eLearning) have become increasingly popular, research into these has also identified the benefits of tailoring learning content to learning styles. However, in games-based learning (GBL), the adaptation based on learning style to enhance the educational experience has not been well researched. For the purpose of this research, a game with three game modes has been developed: 1) non-adaptivity mode; 2) a mode that customises the game according to the student's learning style identified by using a learning style questionnaire; and 3) a mode that has an in-game adaptive system that dynamically and continuously adapts its content according to the student's interactions in the game.This paper discusses the term adaptivity in a GBL context and presents the results of an experimental study investigating the differences in learning effectiveness of the different game modes compared to a paper-based learning. The study was performed with 120 Higher Education students learning the database language SQL (Structured Query Language). The results show that the game developed, regardless of mode, produced better learning outcomes than those who learned from a textbook while adaptive GBL was better in terms of allowing learners to complete the tasks faster than the other two game versions.     

AH-questionnaire: An adaptive hierarchical questionnaire for learning styles

One of the main concerns when providing learning style adaptation in Adaptive Educational Hypermedia Systems is the number of questions the students have to answer. Most of the times, adaptive material available will discriminate among a few categories for each learning style dimension. Consequently, it is only needed to take into account the general tendency of the student and not the specific score obtained in each dimension. In this context, we present AH-questionnaire, a new approach to minimize the number of questions needed to classify student Learning Styles. Based on the Felder-Silverman’s Learning Style Model, it aims at classifying students into categories in spite of providing precise scores. The results obtained in a case study with 330 students are very promising. It was possible to predict students’ learning style preference with high accuracy and only a few questions.     

Intelligent preference selection model based on NRE for evaluating student learning achievement

In order to evaluate student learning achievement, several aspects should be considered, such as exercises, examinations, and observations. Traditionally, such an evaluation calculates a final score using a weighted average method after awarding numerical scores, and then determines a grade according to a set of established crisp criteria. However, this approach lacks the potential to reflect the individual characteristics of a class compared to others. Several researches have used fuzzy techniques to devise practical methods for evaluating student learning achievement to ascertain linguistic terms that are usually used by teachers to assess student learning achievement. However, these approaches are largely based on expert opinions and require complicated computational processes. In this paper, we present a new method for evaluating student learning achievement using an adaptive ordered weighted averaging operator and K-nearest-neighbor classification method. The proposed method simulates the evaluation behavior of teachers when performing a student achievement evaluation based on a norm-referenced evaluation by identifying situations involving the application of intelligence and provides a useful means to award a reasonable grade to students. Furthermore, the proposed method provides a feedback mechanism to update the norm dataset. Therefore, the repetitious use of the feedback mechanism will gradually strengthen the representativeness of the norm dataset.     

Developing an assessment-centered e-Learning system for improving student learning effectiveness

This research used Web-based two-tier diagnostic assessment and Web-based dynamic assessment to develop an assessment-centered e-Learning system, named the ‘GPAM-WATA e-Learning system.’ This system consists of two major designs: (1) personalized dynamic assessment, meaning that the system automatically generates dynamic assessment for each learner based on the results of the pre-test of the two-tier diagnostic assessment; (2) personalized e-Learning material adaptive annotation, meaning that the system annotates the e-Learning materials each learner needs to enhance learning based on the results of the pre-test of the two-tier diagnostic assessment and dynamic assessment. This research adopts a quasi-experimental design, applying GPAM-WATA e-Learning system to remedial Mathematics teaching of the ‘Speed’ unit in an elementary school Mathematics course. 107 sixth-graders from four classes in an elementary school participated in this research (55 male and 52 female). With each class as a unit, they were divided into four different e-Learning models: (1) the personalized dynamic assessment and personalized e-Learning material adaptive annotation group (n = 26); (2) the personalized dynamic assessment and non-personalized e-Learning material adaptive annotation group (n = 28); (3) the non-personalized dynamic assessment and personalized e-Learning material adaptive annotation group (n = 26); and (4) the non-personalized dynamic assessment and non-personalized e-Learning material adaptive annotation group (n = 27). Before remedial teaching, all students took the prior knowledge assessment and the pre-test of the summative assessment and two-tier diagnostic assessment. Students then received remedial teaching and completed all teaching activities. After remedial teaching, all students took the post-test of the summative assessment and two-tier diagnostic assessment. It is found that compared to the e-Learning models without personalized dynamic assessment, e-Learning models with personalized dynamic assessment are significantly more effective in facilitating student learning achievement and improvement of misconceptions, especially for students with low-level prior knowledge. This research also finds that personalized e-Learning material adaptive annotation significantly affects the percentage of reading time students spend on the e-Learning materials they need to enhance learning. However, it does not appear to predict student learning achievement and improvement of misconceptions.     

导师制模式下大学生程序设计创新能力提高的研究

针对国家和学校对于专业教学计划和教学时数不断精简的问题,提出一种基于本科生导师制度模式的程序设计竞赛教学方法改革。以程序设计的在线评判技术与系统为核心,以计算机学生程序设计能力提高为出发点,研究构建面向计算机语言课程的学生自主实践平台,提出了一整套基于现代化的网络技术和通信技术提高学生编程能力的培养方案,探索了计算专业学生编程实践能力的培养模式。     

Interactive augmented reality system for enhancing library instruction in elementary schools

Due to limited budgets and manpower, most elementary schools in Taiwan do not plan or provide library instruction for students. Although students can use libraries, they typically lack the knowledge needed to use library resources effectively. Consequently, students have difficulty finding the books they need and can easily become overwhelmed by the massive amount of information in libraries. Computer-assisted instruction for teaching basic library skills to large numbers of students is an appealing method. Particularly, developing augmented reality (AR) technologies for learning have garnered considerable attention in education research. Many researchers and scholars believe that integrating teaching and AR enhances student learning performance and motivation. This work develops an educational AR system based on situated learning theory, and applies innovative augmented reality interactive technology to a library’s learning environment. Student library knowledge can be enhanced via the proposed augmented reality library instruction system (ARLIS). Experimental results demonstrate that student learning performance is improved significantly by using the proposed ARLIS. Moreover, this work demonstrates that using the proposed ARLIS for library instruction results in the same learning performance as conventional librarian instruction and there is no gender difference on learning performance between the proposed ARLIS and conventional librarian instruction. Moreover, the proposed library instruction system overcomes shortcomings of personal teaching skills of librarians that may adversely affect student learning performance by conveying the same learning content to all students. Additionally, the proposed system results in better learning performance for learners with the field-dependent cognitive style than learners with the field-independent cognitive style. Further, the proposed system provides more benefits in terms of library skills of application and comprehension than conventional librarian instruction. Moreover, the learning performance of students is not affected by their gaming skills. Therefore, student gaming skills do not need to be considered when adopting the proposed system in library instruction programs.     

Investigating the relationship between success factors and student participation in online and blended learning in adult education

Online and blended learning (OBL) is valued, but it also offers challenges. Literature indicates that OBL can enhance access to education and increase flexibility for students. However, the reported dropout rates indicate that student participation in OBL programmes is a concern. Scientifically valid knowledge about how factors that help students participate in OBL are related to student participation is necessary for quality improvement of OBL. This knowledge can help professionals determine what they need to improve in their institution and how to prioritize those improvements. In this study, we report on the validation of a quality instrument with indicators related to quality dimensions present in quality management frameworks and important success factors that aid student participation in OBL. These success factors are as follows: credibility, transparency, flexibility, accessibility, interactivity, personalization, and productivity. The partial least squares structural equation modelling (PLS‐SEM) method is suitable for validating complex models in studies where predictive accuracy is important. According to adult students, all success factors, except flexibility, are important for aiding OBL participation. Adult students perceive that the quality dimensions of learning activities and student support (related to interactivity) deserve priority in improving participation in OBL in adult education.     

Detecting students' perception style by using games

Knowing students' learning styles allows us to improve their experience in an educational environment. Particularly, the perception style is one of the most important dimensions of the learning styles since it describes the way students perceive the world as well as the kind of learning content they prefer. Several approaches to detect students' perception style according to Felder's model have been proposed. However, these approaches exhibit several limitations that make their implementation difficult. Thus, we propose a novel approach to detect the perception style of a student by analyzing his/her interaction with games, namely puzzle games. To carry out this detection, we track how students play a puzzle game and extract information about this interaction. Then, we train a Naive Bayes Classifier to infer the students' perception style by using the information extracted. We have evaluated our proposed approach with 47 Computer Engineering students. Experimental results showed that the perception style was successfully predicted through the use of games, with an accuracy of 85%. Finally, we conclude that games are a promising environment where the students' perception style can be detected.     

Adaptive educational hypermedia accommodating learning styles: A content analysis of publications from 2000 to 2011

Implementing instructional interventions to accommodate learner differences has received considerable attention. Among these individual difference variables, the empirical evidence regarding the pedagogical value of learning styles has been questioned, but the research on the issue continues. Recent developments in Web-based implementations have led scholars to reconsider the learning style research in adaptive systems. The current study involved a content analysis of recent studies on adaptive educational hypermedia (AEH) which addressed learning styles. After an extensive search on electronic databases, seventy studies were selected and exposed to a document analysis. Study features were classified under several themes such as the research purposes, methodology, features of adaptive interventions and student modeling, and findings. The analysis revealed that the majority of studies proposed a framework or model for adaptivity whereas few studies addressed the effectiveness of learning style-based AEH. Scales were used for learning style identification more than automatic student modeling. One third of the studies provided a framework without empirical evaluation with students. Findings on concrete learning outcomes were not strong enough; however, several studies revealed that suggested models influenced student satisfaction and success. Current trends, potential research gaps and implications were discussed.     

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.     

Personalized messages in a brewery educational simulation: Is the personalization principle less robust than previously thought?

The personalization principle, one of the design principles of multimedia learning, states that people learn better from multimedia presentations when instructions are in a conversational style rather than a formal style, possibly due to learners' increased interest. This principle was shown to be robust in short interventions that could be completed within minutes or a few dozen minutes; however, complex digital simulations and games that support the acquisition of complex mental models usually take longer to complete. In this study, we investigate the personalization principle in a new context: in an interactive simulation on the topic of beer brewing, which lasts 2–3 h. Instructions were presented in the Czech language, either in a personalized style, where learners were addressed conversationally by “their grandpa, an owner of the family brewery,” or in a non-personalized, more formal style without the grandpa. In Experiment 1, 26 college students, who interacted with both simulation versions, expressed on average a preference for the personalized version of the simulation. However, some of them worried that personalization could distract them. In Experiment 2 with a between-subject design, the knowledge of 75 predominantly college students was tested by means of retention and transfer tests immediately after completing the simulation and also a month later. Contrary to most previous works, our results showed no difference between the personalized and non-personalized groups in learning achievement, despite the fact that learners who received the personalized treatment voluntarily spent about 20% more time on the simulation. We also applied various measures of the learner's affective state, including Flow Short Scale and PANAS, but – again – no between-group differences were observed. These results indicate that personalization is not always beneficial to learning, which raises important questions for future research. Additional findings suggest that the simulation, no matter the treatment type, was most beneficial to learners with high mathematical abilities and who play computer games frequently, and also to those who liked the simulation more.     

Are learning styles useful indicators to discover how students use Scrum for the first time?

Teaching agile practices is in the cutting-edge of Software Engineering education since agile methodologies are widely used in the industry. An effective strategy to teach agile practices is the use of a capstone project, in which students develop requirements following an agile methodology. To improve students’ learning experience, professors have to keep track and analyze the information generated by the students during the capstone project development. The problem here arises from the large amount of information generated in the learning process, which hinders professors to meet each student’s learning profile. Particularly, to know the students skills and preferences are key aspects on a learner-centered approach of education in order to personalize the teaching. In this work, we aim to discover the relationships between students’ performance along a Scrum-based capstone project and their learning style according to the Felder–Silverman model, towards a first step to build the profiles. To address this issue, we mined association rules from the interaction of 33 Software Engineering students with Virtual Scrum, a tool that supports the development of the capstone project in the course. In the present work we describe promising results in experiments with a case-study.