Learning style detection based on cognitive skills to support adaptive learning environment – A reinforcement approach

Balasubramanian, Vidhya; Anouncia, S. Margret

doi:10.1016/j.asej.2016.04.012

Cited by 24 publications

(11 citation statements)

References 10 publications

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“…Fuzzy logic has been used in [ 11 ] as a means to automatically detect learning styles, taking as input solely learner’s navigational accesses data with promising results for the efficiency and effectiveness of the entire learning process. As reported by other studies [ 12 , 13 ], the learners’ skills and their prior knowledge are the key characteristics that have been used towards the automatic detection of learning styles; both research efforts focused on mapping students’ skills in terms of knowledge of facts and meaning and integration of and application of knowledge being closely related to learning style. In [ 14 ], the authors present an automatic approach for detecting students’ learning style based on web usage mining; specifically, the students’ log files were classified using clustering algorithms with a view to detect their learning style.…”

Section: Related Workmentioning

confidence: 99%

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Troussas

Krouska

Sgouropoulou

et al. 2020

Entropy

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Mobile personalized learning can be achieved by the identification of students’ learning styles; however, this happens with the completion of large questionnaires. This task has been reported as tedious and time-consuming, causing random selection of the questionnaires’ choices, and thus, erroneous adaptation to students’ needs, endangering knowledge acquisition. Moreover, mobile environments render the selection of questionnaires’ choices impractical due to confined mobile user interfaces. In view of the above, this paper presents Learnglish, a fully developed mobile language learning system incorporating automatic identification of students’ learning styles according to the Felder-Silverman model (FSLSM) using ensemble classification. In particular, three classifiers, namely SVM, NB and KNN, are combined based on the majority voting rule. The major innovation of this task, apart from the ensemble classification and the mobile learning environment, is that Learnglish takes as input a minimum number of personal (i.e., age and gender) and cognitive characteristics (i.e., prior academic performance categorized using fuzzy weights), and solely four questions pertaining to the FSLSM dimensions, to identify the learning style. Furthermore, Learnglish incorporates adapted instructional routines to create an individualized learning environment based on students’ learning preferences as determined by their style. Learnglish was fully evaluated with very encouraging results.

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Section: Related Workmentioning

confidence: 99%

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Troussas

Krouska

Sgouropoulou

et al. 2020

Entropy

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“…Percobaan yang dilakukan untuk mengumpulkan data pada penelitian ini adalah dengan melakukan uji coba e-learning. Pada mata pembelajaran dilakukan pengamatan secara langsung untuk melihat aktivitas siswa pada penggunaan e-learning [3]. Dilanjutkan dengan interview kepada setiap siswa dan pengajar mengenai metode pembelajaran menggunakan e-learning [4].…”

Section: Metodologi Penelitianunclassified

Moodle Sebagai Pembelajaran Dalam Sekolah Menengah Atas

Dhika

Destiawati

Sonny

et al. 2019

SENARIS

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Learning methods begin to be less effective because some students do not actively interact with teaching teachers when the learning process takes place and lack of parental supervision. In a centralized learning method like this students are not too required to be independent in learning activities, such as searching for materials and discussion material because everything has been provided by the teacher. Learning Methods E-learning is applied to overcome students' problems in the process of teaching and learning activities in the classroom. Moodle is an application built to support the process of teaching and learning activities by utilizing internet media through a website using the principles of social constructivist pedagogy which will greatly help teachers from all aspects of the teaching and learning process. The research method that will be used is a qualitative descriptive method by using saturated samples where the population is equal to the number of samples. The technique that will be used in the process of collecting data is search and observation. The results of this study are active interactions between students, and also with teachers and parents in the process of teaching and learning activities, forums for discussion between students, and also with teachers. Because e-learning can also be used as a discussion forum, holding online quizzes, and other learning materials, so the learning process will be more flexible and effective for students and lecturers.

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“…Chen [8] study designed a framework based on cognitive and motivational aspects of learning to develop an adaptive e-learning scaffold system. In addition, Balasubramanian and Anouncia [9] study developed a mobile adaptive scaffold model based on the students' cognitive skills and the improvement of knowledge competency level. Hubalovsky, et al [10] study also focused on developing an adaptive e-learning system in accordance with the Bloom's Taxonomy for primary school students.…”

Section: Introductionmentioning

confidence: 99%

Design an Adaptive Mobile Scaffolding System According to Students’ Cognitive Style Simplicity vs Complexity for Enhancing Digital Well-Being

Alhalafawy

Najmi

Zaki

et al. 2021

Int. J. Interact. Mob. Technol.

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The present study is aimed to design an adaptive mobile scaffolding System (AMSS) to provide educational scaffolding to students that is compatible with their cognitive style simplicity vs complexity (CSSC), and to measure the effectiveness of the model in enhancing students' digital well-being (DWB) in the Faculty of education, University of XXXX. Semi-experimental design of two groups was used: the first experimental group received unified mobile scaffold regardless of their cognitive styles. The second experimental group received mobile scaffold that was organized and directed to the students based on their characteristics related to their CSSC. Both experimental groups received mobile scaffold as a complementary component to the lectures delivered for the Technological Innovations in Education course in the General Diploma in Education. The study sample consisted of (71) students who met the requirements and they were randomized to two experimental groups. To examine the effectiveness of the AMSS, a DWB scale was developed. Kelly's scale of CSSC was used to identify students who fell within the scope of this style. The results were statistically analyzed using T-test to compare between the groups and to determine the significance of differences. The results showed the effectiveness of the proposed model of AMSS in developing the students’ DWB compared to the unified scaffold model.

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Learning style detection based on cognitive skills to support adaptive learning environment – A reinforcement approach

Cited by 24 publications

References 10 publications

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Ensemble Learning Using Fuzzy Weights to Improve Learning Style Identification for Adapted Instructional Routines

Moodle Sebagai Pembelajaran Dalam Sekolah Menengah Atas

Design an Adaptive Mobile Scaffolding System According to Students’ Cognitive Style Simplicity vs Complexity for Enhancing Digital Well-Being

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