1,120 research outputs found

    Learning styles based adaptive intelligent tutoring systems: Document analysis of articles published between 2001. and 2016.

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    A novel algorithm for dynamic student profile adaptation based on learning styles

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.E-learning recommendation systems are used to enhance student performance and knowledge by providing tailor- made services based on the students’ preferences and learning styles, which are typically stored in student profiles. For such systems to remain effective, the profiles need to be able to adapt and reflect the students’ changing behaviour. In this paper, we introduce new algorithms that are designed to track student learning behaviour patterns, capture their learning styles, and maintain dynamic student profiles within a recommendation system (RS). This paper also proposes a new method to extract features that characterise student behaviour to identify students’ learning styles with respect to the Felder-Silverman learning style model (FSLSM). In order to test the efficiency of the proposed algorithm, we present a series of experiments that use a dataset of real students to demonstrate how our proposed algorithm can effectively model a dynamic student profile and adapt to different student learning behaviour. The results revealed that the students could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method

    Layered evaluation of interactive adaptive systems : framework and formative methods

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    An Adaptive E-Learning System based on Student’s Learning Styles and Knowledge Level

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    Es besteht eine starke Nachfrage nach einer positiven Applikation zum Lernen, um den strategischen Plan des indonesischen Ministeriums fĂŒr Bildung und Kultur zu fördern, dass die Ratio von Berufsschule höher als die allgemeinbildende Schule werden kann. Die rasante entwicklung der Informations- und Kommunikationstechnologie könnte es ermöglichen, den Lernenden ein computergestĂŒtztes, personalisiertes E-Learning-System zur VerfĂŒgung zu stellen, um die Tatsache zu ĂŒberwinden, dass jeder Lernende seine eigene PrĂ€ferenz hat. Diese Studie bietet ein adaptives E-Learning-System, bei dem zwei Quellen der Personalisierung berĂŒcksichtigt werden: der Lernstil des SchĂŒlers und das Vorwissen. Um die Wirksamkeit des vorgeschlagenen E-Learning-Programms zu untersuchen, werden die Leistungen der SchĂŒler bezĂŒglich der drei niedrigsten Ebenen im kognitiven Bereich (Wissen, VerstĂ€ndnis und Anwendung) in der E-Learning-Gruppe mit denen der traditionellen Unterrichtsgruppe verglichen. Ein weiterer interessanter Bereich ist die sogannte schĂŒlerperspektive Usability-Bewertung und die Beziehung zwischen den Usability-Fragebogen angegebenen Aspekten zu erforschen. Der Entwurfs- und Entwicklungsprozess des adaptiven E-Learning-Systems in dieser Studie berĂŒcksichtigte sowohl das Instruktionsdesign als auch das Software-Engineering. Die erste Phase begann mit der Analyse des Kandidaten der Teilnehmer, des Fachkurses und des Online-Liefermediums. Der nĂ€chste Schritt bestand darin, die Prozedur, die Regelwerk der Adaptation und die BenutzeroberflĂ€che zu entwerfen. Dann wurde Entwicklungsprozess des Lehrsystems auf der Grundlage der aus den vorherigen Phasen gesammelten Daten durchgefĂŒhrt. Die nĂ€chste Phase war die Implementierung des Unterrichtsprogramms fĂŒr die SchĂŒler in einer kleinen Gruppe. Schließlich wurde die E-Learning-Anwendung in drei verschiedenen Teststrategien bewertet: Funktionsbasiertes Testen, Expertenbasierte Bewertung und benutzerperspektivische Bewertung. Die nĂ€chste Aktion ist eine experimentelle Studie, bei der das adaptive E-Learning-System im Lernprozess angewendet wird. An diesem Experiment waren zwei Gruppen beteiligt. Die Experimentalgruppe bestand aus 21 Studenten, die den Unterrichtsfach Digital Simulation mithilfe des adaptiven E-Learning-Systems lernten. Eine andere Gruppe war die Kontrollgruppe, die 21 SchĂŒler umfasste, die dasselbe Unterrichtsfach in der traditionellen Klasse lernten. Es wurden zwei Instrumente verwendet, um die erforderlichen Daten zu erheben. Das erste Instrument bestand aus 30 Multiple-Choice-Fragen, die die kognitiven Ebenen von Wissen, Verstehen und Anwendung enthielten. Dieses Instrument wurde verwendet, um die SchĂŒlerleistung bei dem obengeschriebenen Unterrichtsfach zu bewerten. Das zweite Instrument war der Usability-Fragebogen, der aus 30 4-Punkte-Likert Aussagen bestand. Dieser Fragebogen bestand aus vier Dimensionen nĂ€mlich NĂŒtzlichkeit, Benutzerfreundlichkeit, Lernfreundlichkeit und Zufriedenheit. Mit diesem Fragebogen wurde die Usability der adaptiven E-Learning-Applikation basierend auf die Perspektive des SchĂŒlers bewertet. Der Befund dieser Studie ergab ein ungewöhnliches PhĂ€nomen, bei dem das Ergebnis des Pre-Tests der Kontrollgruppe signifikant höher als Experimentalgruppe. Zum Post-Test Vergleich, obwohl die Leistung der E-Learning Gruppe höher als der von der regulĂ€ren war, war der Unterschied zwischen den beiden statistisch nicht signifikant. Der Vergleich der Punktzahlsteigerung wurde gemacht, um zu untersuchen, welche Behandlungsgruppe effektiver war. Die Ergebnisse zeigten, dass die gesamte Punktzahlsteigerung von der Experimentalgruppe signifikant höher als die von der Kontrollgruppe war. Diese Beweise waren auch im Hinblick auf das Wissen, das VerstĂ€ndnis und die Anwendungsebene des kognitiven Bereichs gĂŒltig. Diese Ergebnisse bestĂ€tigten, dass die Gruppe des adaptiven E-Learning-Systems bezĂŒglich ihrer Leistung effektiver war als die Gruppe der Studenten, die in der traditionellen Klasse lernten. Ein weiterer wichtiger Befund betraf die Bewertung der Usability. Die Punktzahl der Messung wurde anhand verschiedener AnsĂ€tze analysiert und ergab, dass der Usability-Score in allen Aspekten (NĂŒtzlichkeit, Benutzerfreundlichkeit, Lernfreundlichkeit und Zufriedenheit) den akzeptablen Kriterien zuzuordnen ist. DarĂŒber hinaus wurde die Regressionsanalyse durchgefĂŒhrt, um die Beziehung zwischen den Variablen zu untersuchen. Der erste Befund ergab, dass die unabhĂ€ngigen Variablen (NĂŒtzlichkeit, Benutzerfreundlichkeit und Lernfreundlichkeit) gleichzeitig die abhĂ€ngige Variable (Zufriedenheit) beeinflussten. In der Zwischenzeit ergab der Teil t-Test unterschiedliche Ergebnisse. Die Ergebnisse zeigten, dass die variable Benutzerfreundlichkeit die variable Zufriedenheit signifikant beeinflusste. Der variable NĂŒtzlichkeit und die Lernfreundlichkeit wirkten sich indessen nicht signifikant auf die variable Zufriedenheit aus.There is a strong demand for a positive instructional application in order to address the strategic plan of the Ministry of Education and Culture in Indonesia to change the ratio of vocational secondary school to be higher than the general school one. The immense growth of information and communication technology may be possible to provide a computer-based personalized e-learning system to the learners in order to overcome the fact that each student has their own preferences in learning. This study offers an adaptive e-learning system by considering two sources of personalization: the student’s learning style and initial knowledge. In order to investigate the effectiveness of the proposed e-learning program, the students’ achievement in terms of three lowest levels in the cognitive domain (knowledge, comprehension, and application) in the e-learning group is compared with the traditional classroom group. Another area that is interesting to explore is the usability evaluation based on the students’ perspective and the relationship between aspects specified in the usability questionnaire. The design and development process of the adaptive e-learning system in this study was considering both the instructional system design and software engineering. The first phase was started by analyzing the participants’ candidate, the subject course, and the online delivery medium. The next step was designing the procedure, the adaptation set of rules, and the user interface. Then, the process to develop the instructional system based on the data collected from the previous phases was conducted. The next stage was implemented the instructional program to the students in a small group setting. Finally, the e-learning application was evaluated in three different settings: functional-based testing, experts-based assessment, and user-perspective evaluation. The next action is an experimental study by applying the adaptive e-learning system to the learning process. There were two groups involved in this experiment. The experimental group that consisted of 21 students who learned the Digital Simulation course by utilizing the adaptive e-learning system. Another group was the control group that included 21 students who studied the same course through the traditional classroom setting. There were two instruments used to collect the required data. The first instrument contained 30 multiple-choice questions that considered the cognitive levels of knowledge, comprehension, and application. This instrument was used to assess the student achievement of the intended course. The second instrument was the usability questionnaire that consisted of 30 4-point Likert scale statements. This questionnaire was composed of four dimensions, namely usefulness, ease of use, ease of learning, and satisfaction. This questionnaire aimed to evaluate the usability of the adaptive e-learning application based on the student’s perspective. The finding in this study revealed an unusual phenomenon which the pre-test result of the control group was significantly exceeding those of the experimental group. For the post-test score comparison, although there was a higher achievement in the e-learning group than in the regular group, the difference between both achievements was not statistically significant. The comparison in terms of the gain score was conducted in order to investigate which treatment group was more effective. The results indicated that the total gain score achieved by the experimental group was significantly higher than those recorded by the control group. This evidence was also valid with regard to the knowledge, comprehension, and application-level of the cognitive domain. These findings confirmed that the group who utilized the adaptive e-learning system was reported more effective in terms of the achievement score than the group of students who studied in the traditional setting. Another important finding was related to usability evaluation. The measurement score was analyzed through different approaches and revealed that the usability score categorized in the acceptable criteria in all aspects (usefulness, ease of use, ease of learning, and satisfaction). Furthermore, the regression analysis was conducted in order to explore the relation between the variables. The first finding reported that the independent variables (usefulness, ease of use, and ease of learning) simultaneously influenced the dependent variable (satisfaction). In the meantime, the partial t-Test found varying results. The results indicated that the variable ease of use was significantly influenced variable satisfaction. Meanwhile, variable usefulness and ease of learning were not significantly affected variable satisfaction

    ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing

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    Given the rapid ascent of large language models (LLMs), we study the question: (How) can large language models help in reviewing of scientific papers or proposals? We first conduct some pilot studies where we find that (i) GPT-4 outperforms other LLMs (Bard, Vicuna, Koala, Alpaca, LLaMa, Dolly, OpenAssistant, StableLM), and (ii) prompting with a specific question (e.g., to identify errors) outperforms prompting to simply write a review. With these insights, we study the use of LLMs (specifically, GPT-4) for three tasks: 1. Identifying errors: We construct 13 short computer science papers each with a deliberately inserted error, and ask the LLM to check for the correctness of these papers. We observe that the LLM finds errors in 7 of them, spanning both mathematical and conceptual errors. 2. Verifying checklists: We task the LLM to verify 16 closed-ended checklist questions in the respective sections of 15 NeurIPS 2022 papers. We find that across 119 {checklist question, paper} pairs, the LLM had an 86.6% accuracy. 3. Choosing the "better" paper: We generate 10 pairs of abstracts, deliberately designing each pair in such a way that one abstract was clearly superior than the other. The LLM, however, struggled to discern these relatively straightforward distinctions accurately, committing errors in its evaluations for 6 out of the 10 pairs. Based on these experiments, we think that LLMs have a promising use as reviewing assistants for specific reviewing tasks, but not (yet) for complete evaluations of papers or proposals

    An adaptive educational system that caters for combination of two models of learning styles

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    This thesis aimed to explore the affect of combining two models of learning styles (VARK, and Honey and Mumford) in terms of students‘ learning gains and satisfaction. VARK focuses on how the students perceive learning, while Honey and Mumford examines how an individual would like to learn. A web-based educational system was built to test the combination of the two models of learning styles. A study to examine the feasibility of the system was carried out on 129 participants to explore whether the system presented tutorials according to their individual learning styles. A second study to investigate learning gains and user satisfaction was carried out on 149 participants. Satisfaction was divided into three main concepts: usability, preference and perception of learning. Learning gains were tested by giving participants a pre-test, a post-test and a confirmatory test. Participants were divided into four groups and had the lesson presented according to one learning style of either the VARK or Honey & Mumford model, both of the participants‘ learning styles or with no personal customization. The results found that participants who used the two models of learning styles showed higher learning gains and had higher levels of satisfaction across all three factors; compared to those using only one or no learning style. Furthermore, those using only one learning style showed higher learning gains and had higher levels of satisfaction than those with no learning style. The application of these findings would be of benefit to educational institutions‘ decision makers, educators, students and e-learning designers. Adaptation is a key feature of the system of research. It is intended for future work; preliminary research has shown that the users profile and learning item will change over time. This important finding is worth exploring in future research

    Adaptation based on learning style and knowledge level in e-learning systems

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    Although there have been numerous attempts to build and evaluate adaptive e-learning systems, they tend to be limited in scope, and suffer from a lack of carefully designed and controlled experimental evaluations of their effectiveness and usability. This thesis addresses these issues through the implementation of an adaptive e-learning system and its experimental validation. The design of an adaptive framework and the specific instantiation of its components into a configurable adaptive e-learning system are presented. The domain model of the system deals with computer security. The learner model incorporates the information perception dimension of the Felder-Silverman model of learning style and also knowledge level. The adaptation model generates personalised learning paths and offers adaptive guidance and recommendation. The thesis also provides an empirical evaluation through three controlled experiments to investigate the effect of different forms of adaptation. Rigorous experimental design, careful investigation and precise reporting of results are taken into account in all the three experiments. The findings indicate that matching the sequence of learning objects to the information perception learning style yields significantly better learning outcome and learner satisfaction than non-matching sequences. They also indicate that adaptation based on the combination of the information perception learning style and knowledge level yields significantly better learning outcome (both in the short- and long-term) and learner satisfaction than adaptation based on either of these learner characteristics alone; this combination is also marked by a significantly higher level of perceived usability compared to a non-adaptive version of the e-learning system
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