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Towards a Chatbot-Based Learning Object Recommendation: A Comparative Experiment
Abstract
An adaptive learning system aims to provide learning that is adapted to a learner’s current status, different from the traditional classroom experience. A key element of an adaptive learning system is the recommendation system, which provides the most suitable resources based on learner profiles. Recommending the most appropriate learning resources to learners has always been a challenge in the field of e-learning. Thus, learners may have difficulties in choosing the appropriate material when faced with a large volume of recommended material during their learning process. This challenge led us to implement a chatbot to help learners improve their learning experience and knowledge. New solutions use artificial intelligence (AI) techniques such as machine learning (ML) and natural language processing (NLP). The use of our chatbot integrated in Moodle, named LearningPartnerBot, provides learners a personalized recommendation of learning objects according to two strategies, one based on their knowledge level (KL) and the other based on their learning style (LS). The objective of this article is to compare the learning outcomes obtained after the realization of the two experiments based on these two approaches centered mainly on the use of the LearningPartnerBot. Consequently, the approach of recommending learning objects based on the knowledge level gave promising results by guaranteeing a more adapted learning to the learners.KeywordsE-learningLearning Object RecommendationExperimental DesignAdaptive LearningChatbot
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Pre-evaluation of the learner's level is a common learning strategy designed to determine the prior knowledge and skills of learners. A pre-evaluation is carried out at the beginning of the course and based on the results obtained, personalized resources will be provided that respond to individual learner needs. This paper presents a pre-evaluation for a C programming language course by providing, at the end of the quiz, a personalized formative feedback and recommendation to the learners. We have developed our conversational chatbot named QuizCbot, which allows learners to go directly to the parts where they need the most help through the personalized feedback provided to them, including their final scores, the questions they answered correctly and the questions they answered incorrectly with the correct answer and explanation. Hence, the chatbot makes a recommendation on the concepts in which the learner did not obtain the average, identifying the concepts not mastered where the learner needs more (or less) support. Determining what learners know and don't know can help to improve the learning experience. We have integrated our QuizCbot chatbot, which is based on Natural Language Understanding (NLU), into the Moodle learning environment.
Electronic learning (e-learning) is a broader approach to learning that brings new opportunities for learning and teaching in many fields of education far from the traditional classroom environment. Over the past decades, research in the field indicates a proliferation of e-learning contents and discrepancies that affect interoperability patterns in education for students and teachers; however, little has been done to assess the usability of e-learning systems. From a different perspective, this study aims to provide information on the numerous findings relating to the cumulative results of e-learning in education. This systematic review uses a full protocol with the aim of standardizing and specifying all the procedures adopted to collect and code 99 academic articles from 2010 to 2018 with keywords: education and e-learning. The text analysis as conducted using the qualitative software Leximancer to extract meaning from the large number of articles retrieved. The results highlight four dominant themes, namely education systems and learning issues that in turn promote student behaviours and the use of online learning tools.
This research contributes towards providing research propositions that can be used in a cogent theoretical framework and, based on the analysis, we also propose a new definition of e-learning.
E-Learning has become more and more popular in recent years with the advance of new technologies. Using their mobile devices, people can expand their knowledge anytime and anywhere. ELearning also makes it possible for people to manage their learning progression freely and follow their own learning style. However, studies show that E-Learning can cause the user to experience feelings of isolation and detachment due to the lack of human-like interactions in most E-Learning platforms. These feelings could reduce the user’s motivation to learn. In this paper, we explore and evaluate how well current chatbot technologies assist users’ learning on E-Learning platforms and how these technologies could possibly reduce problems such as feelings of isolation and detachment. For evaluation, we specifically designed a chatbot to be an E-Learning assistant. The NLP core of our chatbot is based on two different models: a retrieval-based model and a QANet model. We designed this two-model hybrid chatbot to be used alongside an E-Learning platform. The core response context of our chatbot is not only designed with course materials in mind but also everyday conversation and chitchat, which make it feel more like a human companion. Experiment and questionnaire evaluation results show that chatbots could be helpful in learning and could potentially reduce E-Learning users’ feelings of isolation and detachment. Our chatbot also performed better than the teacher counselling service in the E-Learning platform on which the chatbot is based.
The topic of engagement has been attracting increasing amounts of attention in the field of e-learning. Research shows that multifarious benefits occur when students are engaged in their own learning, including increased motivation and achievement. Previous studies have proposed many scales for measuring student engagement. However, very few have been developed to measure engagement in e-learning environments. Thus, developing an instrument for measuring student engagement in e-learning environments is the purpose of this study. The participants of this study were 737 Korean online university students. Initial items were designed based on the literature. The instrument items were reduced from an initial 48 to 24 items after obtaining expert opinion and then validity and reliability analysis. Exploratory and confirmatory factor analyses were also conducted. Six factors, including psychological motivation, peer collaboration, cognitive problem solving, interaction with instructors, community support, and learning management emerged in the 24-item scale. This scale is expected to help instructors and curriculum designers to find conditions to improve student engagement in e-learning environments, and ultimately prevent students from dropping out of online courses.
Adaptive user interfaces (UIs) change their presentation at runtime to remain usable in different contexts-of-use. Such changes can cause discrepancies between the UI and static help materials, e.g., videos and screenshots, thereby negatively impacting the latter's usefulness (usability and utility). This paper presents Contextual Help for Adaptive INterfaces (CHAIN), which is an approach for developing model-driven contextual help that maintains its usefulness across UI adaptations. This trait is achieved by interpreting the help models at runtime and overlaying instructions on the running adapted UI. A language called Contextual Help for Adaptive INterfaces eXtensible Markup Language (CHAINXML) and a visual notation were developed for expressing and depicting help models. A technique was also devised for presenting CHAIN help models over legacy applications, whether or not their source-code is available. A supporting tool was developed as an extension to Cedar Studio. This work was empirically evaluated in two studies. The first study performed a preliminary evaluation of CHAIN's visual notation. The second study evaluated CHAIN's strengths and shortcomings after using it to develop help for real-life adaptive UIs. The results gave a positive indication about CHAIN's technical qualities and provided insights that could inform future work.
With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities.
The Index of Learning Styles© (ILS) is an instrument designed to assess preferences on the four dimensions of the Felder-Silverman learning style model. The Web-based version of the ILS is taken hundreds of thousands of times per year and has been used in a number of published studies, some of which include data reflecting on the reliability and validity of the instrument. This paper seeks to provide the first comprehensive examination of the ILS, including answers to several questions: (1) What are the dimensions and underlying assumptions of the model upon which the ILS is based? (2) How should the ILS be used and what misuses should be avoided? (3) What research studies have been conducted using the ILS and what conclusions regarding its reliability and validity may be inferred from the data?
Learning styles are considered to be one of the factors that need to be taken into account in developing adaptive learning systems. However, few studies have been conducted to investigate if students have the ability to choose the best-fit e-learning systems or content presentation styles for themselves in terms of learning style perspective. In this paper, we aim to investigate these issues by using two versions of an educational game developed based on the sequential/global dimension of the learning style proposed by Felder and Silverman. The experimental results showed that the choices made by the students were not related to their cognitive process or learning style; instead, most students made their choices by intuition based on personal preferences. Moreover, the students who learned with learning style-fit versions showed significantly better learning achievement than those who learned with non-fit versions. Consequently, it is concluded that students preferring one game over another does not necessarily mean that they will learn better with that version, revealing the importance and necessity of developing adaptive learning systems based on learning styles. © International Forum of Educational Technology & Society (IFETS).
Researchers have found that classroom environment has close relationship to students' learning performance. When considering technology enriched classroom environment, researches are mainly on the psychological environment and the measurement of the environment. While as technology integrated in classroom, the physical classroom environment should be investigated to facilitate students' effective and engaged learning. First we carry out a survey on the current technology enriched classroom, after that we sample the Technology Involved Classroom (TIC) and Technology Uninvolved Classroom (TUC) to compare the differences between the two kinds of classroom; then we do the classroom observation and interview with teachers; finally based on the analysis of these data, we propose some solutions for optimizing the classroom environment to facilitate technology enriched learning in China.
Traditional e-learning systems have been, typically, designed for a generic learner, irrespective of individual knowledge, skills and learning styles. In contrast, adaptive e-learning systems can enhance learning by taking into account different learner characteristics and by personalising learning material. Although a large number of systems incorporating learning style have been deployed, there is a lack of comprehensive, comparative evaluations. This paper attempts to bridge this gap by comparing a number of adaptive e-learning systems. It considers three main perspectives: the learner model, the domain model and the adaptation model. A set of criteria is generated for each perspective, and applied to a representative sample of adaptive e-learning systems.
This article presents a systematic framework characterizing adaptive systems.
Adaptive systems are those that can appropriately modify their behavior to fit the current context. This concept is appealing because it offers the possibility of creating computer assistants that behave like good human assistants who can provide what is needed without being asked. However, the majority of adaptive systems have been experimental rather than practical because of the technical challenges in accurately perceiving and interpreting users' current cognitive state; integrating cognitive state, environment, and task information; and using it to predict users' current needs. The authors anticipate that recent developments in neurological and physiological sensors to identify users' cognitive state will increase interest in adaptive systems research and practice over the next few years.
To inform future efforts in adaptive systems, this work provides an organizing framework for characterizing adaptive systems, identifying considerations and implications, and suggesting future research issues.
A two-part framework is presented that (a) categorizes ways in which adaptive systems can modify their behavior and (b) characterizes trigger mechanisms through which adaptive systems can sense the current situation and decide how to adapt.
The framework provided in this article provides a tool for organizing and informing past, present, and future research and development efforts in adaptive systems.
Directions Please provide us with your full name. Your name will be printed on the information that is returned to you. Full Name For each of the 44 questions below select either "a" or "b" to indicate your answer. Please choose only one answer for each question. If both "a" and "b" seem to apply to you, choose the one that applies more frequently. When you are finished selecting answers to each question please select the submit button at the end of the form. I understand something better after I (a) try it out. (b) think it through. 1. I would rather be considered (a) realistic. (b) innovative. 2. When I think about what I did yesterday, I am most likely to get (a) a picture. (b) words. 3. I tend to 4.
Adaptive hypermedia is a new direction of research within the area of adaptive and user model-based interfaces. Adaptive hypermedia (AH) systems build a model of the individual user and apply it for adaptation to that user, for example, to adapt the content of a hypermedia page to the user's knowledge and goals, or to suggest the most relevant links to follow. AH systems are used now in several application areas where the hyperspace is reasonably large and where a hypermedia application is expected to be used by individuals with different goals, knowledge and backgrounds. This paper is a review of existing work on adaptive hypermedia. The paper is centered around a set of identified methods and techniques of AH. It introduces several dimensions of classification of AH systems, methods and techniques and describes the most important of them.
Students are characterized by different learning styles, focusing on different types of information and processing this information in different ways. One of the desirable characteristics of a Web-based education system is that all the students can learn despite their different learning styles. To achieve this goal we have to detect how students learn: reflecting or acting; steadily or in fits and starts; intuitively or sensitively. In this work, we evaluate Bayesian networks at detecting the learning style of a student in a Web-based education system. The Bayesian network models different aspects of a student behavior while he/she works with this system. Then, it infers his/her learning styles according to the modeled behaviors. The proposed Bayesian model was evaluated in the context of an Artificial Intelligence Web-based course. The results obtained are promising as regards the detection of students’ learning styles. Different levels of precision were found for the different dimensions or aspects of a learning style.
This paper outlines attempts to enhance student learning by
addressing different learning styles through course hypermedia. Students
learn by a variety of different learning styles. Previously, instructors
were unable to effectively address these different learning styles
outside the classroom. Two approaches were developed to address this
problem. The first approach was the development of hypermedia
courseware. This provided a wide variety of tools which students could
use to prepare for lessons. In this way students retained complete
control over how they prepared for a lesson and could choose those
hypermedia tools that were most conducive to their learning. An
assessment of the multimedia and hypertext documents in the course
revealed that the value of a particular multimedia tool to a student
varied widely. Each student was traversing the course material according
to his/her unique learning style. Unfortunately, the plethora of tools
confused some students because they were uncomfortable making active
choices of what course material would be most conducive to their
learning. As a result, a second approach was adopted. An adaptive
hypermedia interface was developed that provided dynamic tailoring of
the presentation of course material based on the individual student's
learning style. By tailoring the presentation of material to the
student's learning style, the authors believe students learned more
efficiently and more effectively
Learning styles approach reinforces developing the suitable instructional materials for students. Every student has a different yet personalized learning style. The research aims to investigate the learning styles preferences of common first year students at Umm Al-Qura University in Makkah, Kingdom of Saudi Arabia, and it explores if there is a relationship between students' preferred learning styles and their learning motivation. The VARK (Visual, Auditory, Read/Write, and Kinesthetic) questionnaire was used to investigate learning style preferences. Correlative Descriptive methodology was used to identify the students' learning styles preferences according to the different academic disciplines (Medical, Scientific "Engineering", and Administrative). The research sample consisted of 105 male students selected randomly from the three sections at deanship of common first year. Overall, the results revealed that the preferred learning styles according to VARK questionnaire was Kinesthetic-learning style. Results revealed that about half of the whole students (63%) preferred the Kinesthetic learning style-(uni-model)-followed by the Read/Write style (23.4%) then the visual style (7.4%), the auditory learning style comes at the end with (6.2%). In terms of remaining of the whole sample, the results revealed that students preferred more than one learning style (mixed-modal). Regarding the academic-discipline, the preferred learning styles as follow, the medical students' group preferred Kinesthetic-learning style, at the similar, the scientific students' group preferred as well Kinesthetic-learning style, however, the administrative students' group preferred Read/Write learning style. The results revealed that, there is a significant relationship between learning styles and students' learning motivation. As per, the highest correlation belongs to kinesthetic learning styles, kinesthetic students have more motivation for higher education. The results also revealed the above those students do not have the same learning styles and experience of learning techniques. The results of this research are expected to determining learning styles of the students that should contribute augmenting the learning process & outcomes in terms of the students' success through delivering appropriate e-learning environment including the suitable activities, instructional media and provide better teaching practice within such learning environments. The research can be contributed to augment learning outcomes at Umm Al-Qura University.
Chatbots have been around for years and have been used in many areas such as medicine or commerce. Our focus is on the development and current uses of chatbots in the field of education, where they can function as service assistants or as educational agents. In this research paper, we attempt to make a systematic review of the literature on educational chatbots that address various issues. From 485 sources, 80 studies on chatbots and their application in education were selected through a step‐by‐step procedure based on the guidelines of the PRISMA framework, using a set of predefined criteria. The results obtained demonstrate the existence of different types of educational chatbots currently in use that affect student learning or improve services in various areas. This paper also examines the type of technology used to unravel the learning outcome that can be obtained from each type of chatbots. Finally, our results identify instances where a chatbot can assist in learning under conditions similar to those of a human tutor, while exploring other possibilities and techniques for assessing the quality of chatbots. Our analysis details these findings and can provide a solid framework for research and development of chatbots for the educational field.
In this study, the trends and developments of technology-enhanced adaptive/personalized learning have been studied by reviewing the related journal articles in the recent decade (i.e., from 2007 to 2017). To be specific, we investigated many research issues such as the parameters of adaptive/personalized learning, learning supports, learning outcomes, subjects, participants, hardware, and so on. Furthermore, this study reveals that personalized/adaptive learning has always been an attractive topic in this field, and personalized data sources, for example, students’ preferences, learning achievements, profiles, and learning logs have become the main parameters for supporting personalized/adaptive learning. In addition, we found that the majority of the studies on personalized/adaptive learning still only supported traditional computers or devices, while only a few studies have been conducted on wearable devices, smartphones and tablet computers. In other words, personalized/adaptive learning has a significant number of potential applications on the above smart devices with the rapid development of artificial intelligence, virtual reality, cloud computing and wearable computing. Through the in-depth analysis of the trends and developments in the various dimensions of personalized/adaptive learning, the future research directions, issues and challenges are discussed in our paper.
An adaptive learning environment provides personalised information to the learner through self-directed study. An adaptive learning environment model can be subdivided into a learner model, domain model, instructional model and adaptive engine. Personal traits comprise part of the components in a learner model and can be identified either explicitly or implicitly in an adaptive learning environment. In such an environment, the e-learning system should adapt to a learner's needs. However, even though academic research on adaptive learning environments has increased, the field lacks a comprehensive literature analysis of learners’ personal traits in these environments. This study conducts a systematic literature review to identify the most commonly used personal traits in modelling the learner and the existing techniques suitable for identifying personal traits in an adaptive learning environment. A total of 140 articles spanning the years 2010–2017 are initially reviewed, from which 78 are selected based on the inclusion and exclusion criteria relevant to this study. This study provides an overview of learners’ personal traits and the techniques used to identify them to provide a basis for improving adaptive learning environments. The findings indicate that most of the previous works used a learning style from the cognition learning domain category to model individual personal traits, while the computer-based detection technique was commonly applied to identify a learner's personal traits in adaptive learning environments. This study reveals the common learner characteristics used to develop learner models and the techniques for implementing such models. The findings of this paper can guide other researchers to recognise various personal traits and the identification technique for further studies, as well as assist developers in the development of the adaptive learning system.
Elective course selection often challenges students to make decisions concerning their academic interests and other practical issues such as graduation plan, class scheduling, and difficulty of course content. Conversations with academic advisors and peers are usually considered as a useful process for obtaining official and informal information, rearranging priorities, and making compromise in the decision. The paper describes the design and development of a conversational agent called EASElective for elective course selection. EASElective is designed to complement existing academic advising services with an online natural language interactive interface that will support a conversation on topics from basic official course data to informal students’ opinions. The major design components of EASElective include intent detection, routines for conversation management, dialogue design, sustainable students’ opinion collection and analysis, and course information management. The paper also describes a study on the perceived usefulness of EASElective. The findings were found to be largely positive and EASElective has unique functions and characteristics when compared to other conventional academic advising services.
Adaptive online advertising is a rapidly expanding marketing tool that delivers personalised messages and adverts to Internet users. At a time when the Internet is burgeoning, many websites use an adaptation process to tailor their advertisements, however, often in an adhoc manner. Thus, a new model that guarantees a systematic integration of adaptive features on existing business websites has become an urgent requirement to satisfy customers. This paper aims to solve this issue, by presenting an innovative model for e-advertising adaptation: the Layered Adaptive Advertising Integration (LAAI). LAAI is building upon previous models and frameworks from different domains, by selecting and adding novel features appropriate for eadvertising. Based on this model, a new adaptation system –AEADS – is developed, to test and evaluate the LAAI model. This research also reports on the perception on the methods towards obtaining generalisation, portability and efficiency, as proposed by the LAAI model, by evaluating how a range of businesses are enabled to adapt their advertisements based on user profiles and behaviours.
The major requirement of present e-learning system is to provide a personalized interface with adaptiveness. This is possible to provide by analyzing the learning behaviors of the learners in the e-learning portal through Web Usage Mining (WUM). In this paper, a method is proposed where the learning behavior of the learner is captured using web logs and the learning styles are categorized according to Felder-Silverman Learning Style Model (FSLSM). Each category of FSLSM learner is provided with the respective content and interface that is required for the learner to learn. Fuzzy C Means (FCM) algorithm is used to cluster the captured data into FSLSM categories. Gravitational Search based Back Propagation Neural Network (GSBPNN) algorithm is used to predict the learning styles of the new learner. This algorithm is a modification of basic Back Propagation Neural Network (BPNN) algorithm that calculates the weights using Gravitation Search Algorithm (GSA). The algorithm is validated on the captured data and compared using various metrics with the basic BPNN algorithm. The result shows that the performance of GSBPNN algorithm is better than BPNN. Based on the identified learning style, the adaptive contents and interface can be provided to the learner.
E-learning environments are mainly based on a range of delivery and interactive services. Web-based personalized learning recommender systems can, as a kind of services in e-learning environment, provide learning recommendations to students. This research proposes a framework of a personalized learning recommender system, which aims to help students find learning materials they would need to read. Two related technologies are developed under the framework: one is a multi-attribute evaluation method to justify a student's need, and another is a fuzzy matching method to find suitable learning materials to best meet each student need. The implementation of this proposed personalized learning recommender system can support students online learning more effectively and assist large class online teaching with multi-background students.
The studies on creating learning environments based on differences in learning styles have gained importance in recent years. Learning styles are one of the most important parameters in determining individual differences. Accordingly, traditional web-based learning environments have been replaced by individualized adaptive e-learning environments on the basis of learning styles which are more innovative. This study deals with the content analysis of the recent studies on Adaptive Educational Hypermedia (AEH) based on learning styles. 69 articles published from 2005 to 2014 were obtained through a comprehensive and detailed review. Afterwards, these studies were subjected to document analysis. The studies were categorized under the titles of purpose, nature, method, characteristics of examinees, level, data collection tool, learner modelling, learning styles, subject, and findings. Some of the studies offered a framework or proposed a model for AEH while others focused on the influence of AEH on academic achievement and learning outputs as well as learning satisfaction. This study examines the existing tendencies and gaps in the literature and discusses the potential research topics.
Learning styles which refer to students’ preferred ways to learn can play an important role in adaptive e-learning systems. With the knowledge of different styles, the system can offer valuable advice and instructions to students and teachers to optimise students’ learning process. Moreover, e-leaning system which allows computerised and statistical algorithms opens the opportunity to overcome drawbacks of the traditional detection method that uses mainly questionnaire. These appealing reasons have led to a growing number of researches looking into the integration of learning styles and adaptive learning system. This paper, by reviewing 51 studies, delves deeply into different parts of the integration process. It captures a variety of aspects from learning styles theories selection in e-learning environment, online learning styles predictors, automatic learning styles classification to numerous learning styles applications. The results offer insights into different developments, achievements and open problems in the field. Based on these findings, the paper also provides discussion, recommendations and guidelines for future researches.
This paper constitutes a literature review on student modeling for the last decade. The review aims at answering three basic questions on student modeling: what to model, how and why. The prevailing student modeling approaches that have been used in the past 10 years are described, the aspects of students’ characteristics that were taken into consideration are presented and how a student model can be used in order to provide adaptivity and personalisation in computer-based educational software is highlighted. This paper aims to provide important information to researchers, educators and software developers of computer-based educational software ranging from e-learning and mobile learning systems to educational games including stand alone educational applications and intelligent tutoring systems. In addition, this paper can be used as a guide for making decisions about the techniques that should be adopted when designing a student model for an adaptive tutoring system. One significant conclusion is that the most preferred technique for representing the student’s mastery of knowledge is the overlay approach. Also, stereotyping seems to be ideal for modeling students’ learning styles and preferences. Furthermore, affective student modeling has had a rapid growth over the past years, while it has been noticed an increase in the adoption of fuzzy techniques and Bayesian networks in order to deal the uncertainty of student modeling.
Widespread use of the Web and other Internet technologies in postsecondary education has exploded in the last 15 years. Using a set of items developed by the National Survey of Student Engagement (NSSE), the researchers utilized the hierarchical linear model (HLM) and multiple regressions to investigate the impact of Web-based learning technology on student engagement and self-reported learning outcomes in face-to-face and online learning environments. The results show a general positive relationship between the use the learning technology and student engagement and learning outcomes. We also discuss the possible impact on minority and part-time students as they are more likely to enroll in online courses.
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.
A student might find a certain representational format (e.g., diagram, text) more attractive than other formats for learning. Computer technology offers opportunities to adjust the formats used in learning environments to the preferences of individual learners. The question addressed in the current study was: does the match between a student’s preference regarding the format of learning materials have any relation with performance when learning with a specific format? For example, do learners with a preference for visual materials indeed perform better with visual learning materials? In a study with a pre-test post-test design, 48 participants were randomly assigned to one of two conditions. Both conditions completed a mathematical learning task about combinatorics and probability theory. In one condition learning materials were mainly diagram-based in the other condition they were mainly text-based. Afterward, the relations between cognitive style (visualizers–verbalizers), cognitive abilities (e.g., spatial and verbal ability), and learning performance were examined. The findings showed that cognitive style and learning outcomes were unrelated, for example, learners with a preference for visual materials do not necessarily perform better with visual learning materials. Learning results seem to be influenced by cognitive ability (in particular spatial visualization) and the extent to which a format affords cognitive processing, rather than a match between used and preferred format. It is argued that students should not choose on the basis of their preference, because it might lead them to selecting a format that is less effective for learning.
State of the art of adaptivity in e-learning platforms
- D Hauger
- M Augstein
The relationship between learning-styles and learning motivation of students at Umm Al-Qura university
- H A El-Sabagh
- P Brusilovsky
Lightweight domain modeling for adaptive web-based educational system
- M Simko
- M Bielikova