A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments

Aissaoui, Ouafae El; Madani, Yasser El Alami El; Oughdir, Lahcen; Allioui, Youssouf El

doi:10.1007/s10639-018-9820-5

Cited by 63 publications

(39 citation statements)

References 24 publications

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“…The authors further advised that an ILS was meant to help the instructors realize a well-adjusted course instruction and to enable students to understand their learning strengths and weaknesses. To-date FSLSM has been used in several studies involving learner behavior identification in online multimedia systems such as (Aissaoui et al 2018;Abdelhadi et al 2019;Ferreira et al 2019). Thus, the learning style model is well researched and validated with several online learning systems.…”

Section: Felder-silverman Learning Style (Fslsm) and Cognitive Traits Model (Ctm)mentioning

confidence: 99%

Learner behavior prediction in a learning management system

2020

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Learning Management Systems (LMS) lack automated intelligent components that analyze data and classify learners in terms of their respective characteristics. Manual methods involving administering questionnaires related to a specific learning style model and cognitive psychometric tests have been used to identify such behavior. The problem with such methods is that a learner can give inaccurate information. The manual method is also time-consuming and prone to errors. Although literature reports complex models predicting learning styles, only a few have used machine learning methods such as an artificial neural network (ANN). The primary objective of this study was to design, develop, and evaluate a model based on machine learning for predicting learner behavior from LMS log records. Approximately 200,000 log records of 311 students who had accessed e-Learning courses for a 15-week semester were extracted from LMS to create a dataset. Machine learning concepts were identified from the log records. The dataset was split into training and testing sets. A model using the artificial neural network algorithm was designed and implemented using an r-studio programming language. The model was trained to predict learner behavior and classify each student. The prediction success rate of 0.63, 0.67, 0.64, 0.65, 0.26, 0.64 accuracy, precision, recall, f-score, kappa, and Area Under the Curve (AUC) respectively were recorded. This demonstrates that the model after full validation can be relied on to identify learner behavior.

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Section: Felder-silverman Learning Style (Fslsm) and Cognitive Traits Model (Ctm)mentioning

confidence: 99%

Learner behavior prediction in a learning management system

2020

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“…The principles and practices of experiential learning have been widely adopted in models, programs to conduct academic courses such as competency-based education (Miettinen, 2000;Ahmed, 2018), professional training (Richard, Cowen & Kolb, 1995;Akhigbe, 2018;Amolloh, Lilian & Wanjiru, 2018;Tiessen, Grantham & Cameron, 2018;Stefano, Ruvolo & Mauro, 2019), college programs for adult learners, and prior learning assessment (Simosko, 1998;Andrews, Brown & Mesher, 2018;Awidi & Paynter, 2019;Bhati & Song, 2019), service learning (Angela, Bruce, Kevin, Mandar & Kurtis, 2011;Aissaoui, Alami, Oughdir & Allioui, 2018), problem-based learning (Bethell & Morgan, 2011;Gomez & Nasir, 2019;Harfitt, 2019;Stephanie, Lockwood, Hill & Randell, 2020;Setiyani, Harnanik, Lianighsih & Susilowati, 2020), action learning (Louise, 1994). The experiential lessons provide students with an opportunity to experience concepts, give students a more productive, more meaningful understanding of course concepts, and of how they operate in the real world.…”

Section: The Process Of Instruct Students To Perform Experiential Learning Activities In the Schoolsmentioning

confidence: 99%

Guide Pedagogical Students to Design and Organize Experience-based Learning Activities in Schools

Nguyen

Mậu

2020

IJLTER

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In Vietnam, experiential activities and experience-based learning models were first introduced in 2018 into the general education curriculum and they will be applied when implementing this program. Therefore, the teacher training program at university of education should meet the requirements of general education reform. In particular, it is essential to guide pedagogical students to design and organise experiential learning activities in the school. This study describes the design, implementation, and evaluation of a course of guiding to design and organise experience-based learning in schools for Vietnamese pedagogical students who will plan to implement experience-based learning activities into their teaching. A course in a 60-hour instructional process (including 30 hours of self-studying) with 5 activities designed to be experiential, enables our students to simultaneously explore and practice experience-based learning activities. The results of designing and organising 55 experience-based learning activity plans were obtained from one hundred and nine participants of the Faculty of Biology, Thai Nguyen University of Education, Vietnam who assessed the instructional process beneficially. The research group also proposed five effective measures to organise experiential learning in high school. This tutorial process will continue to be improved and implemented in subsequent courses to contribute to the renovation of higher education and meet the general educational improvement in Vietnam.

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“…This study adopted a hybrid of the Felder-Silverman Learning Styles Model (FSLSM) and Cognitive Traits Model (CTM) as theoretical frameworks. Analysis from the literature shows the two are the most researched and validated with LMS to model CT ( Kinshuk, Lin and Mcnab, 2004 , 2006 ; Lin et al., 2007 ; Lin and Kao, 2018 ) and LS ( Graf et al., 2009 ; Graf et al., 2013 ; Aissaoui et al., 2018 ) thus acceptable.…”

Section: Introductionmentioning

confidence: 99%

Identifying learning styles and cognitive traits in a learning management system

Lwande

Muchemi

Oboko

2021

Heliyon

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Investigating learner behavior is an increasingly important research topic in online learning. Learning styles and cognitive traits have been the subjects of research in this area. Although learning institutions use Learning Management Systems such as Moodle, Claroline, and Blackboard to facilitate teaching, the platforms do not have features for analyzing data and identifying behavior such as learning styles and cognitive traits. Instead, they only produce certain statistical reports from the daily access records. Even though complex models have been proposed in the literature, most studies are based on a single behavior such as learning styles or cognitive traits but not both. Only a few have investigated a combination of cognition-based theories such as working memory capacity and psychology-based ones such as learning styles. Thus, this study sought to answer the research question of whether it was possible to establish a methodology for the estimation of learning styles and cognitive traits from a learning management system. The study combined the Felder-Silverman Learning Style Model and Cognitive Trait Model as theoretical frameworks to identify behavior in a Learning Management System. This study designed a model for extracting records from Learning Management Systems access records to estimate learning style and cognitive traits. From this, a prototype was developed to estimate the learning style and cognitive traits for each student. The model was evaluated by administering manual tools to students in a classroom environment then comparing the results gathered against those estimated by the model. The results analyzed using Kappa statistics demonstrated the interrater reliability results were moderately in agreement. Taken together, these results suggest that it is possible to estimate the learning styles and cognitive traits of a learner in a Learning Management System. The information generated by the model can be used by tutors to provide a conducive online learning environment where learners with similar behavior ask each other for help. This can reduce the teaching load for online tutors because learners themselves act as a teaching resource. Information on learning styles and cognitive styles can also facilitate online group formation by isolating the individual factors that contribute to team success.

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A fuzzy classification approach for learning style prediction based on web mining technique in e-learning environments

Cited by 63 publications

References 24 publications

Learner behavior prediction in a learning management system

Learner behavior prediction in a learning management system

Guide Pedagogical Students to Design and Organize Experience-based Learning Activities in Schools

Identifying learning styles and cognitive traits in a learning management system

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