Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning

Flores, Anibal; Alfaro, Luis; Herrera, Jose

doi:10.1109/edunine.2019.8875800

Cited by 4 publications

(5 citation statements)

References 9 publications

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“…15 shows a better academic performance of hybrid CBR+RL model proposed "in press" [2], we attribute this difference with respect to proposal models to the use of optimal sequences in a complementary manner when the CBR success case base does not contain enough cases. However, despite not having managed to overcome the results of the CBR + RL model "in press" [2], the results of the proposal model 1 and proposal model 2 are very promising, since despite the small number of cases (55) the academic results were very close to the CBR + RL model, we consider that, with a greater number of cases, results of the proposal model 1 could equal or exceed the hybrid model. In addition, it is necessary to work with larger samples, 10 or 11 students are not enough.…”

Section: A Academic Resultsmentioning

confidence: 87%

“…Results of proposal models in this work were compared with proposal model of paper "in press" [2], Fig. 15 shows a better academic performance of hybrid CBR+RL model proposed "in press" [2], we attribute this difference with respect to proposal models to the use of optimal sequences in a complementary manner when the CBR success case base does not contain enough cases.…”

Section: A Academic Resultsmentioning

confidence: 99%

“…In recent years, a huge number of sites have appeared and offer online training and education services, such as: coursera, udacity, udemy, etc. "in press" [2]; the offered courses in these sites, generally MOOCs, which are not far from a traditional classroom course, in the sense that these courses have been planned or prepared considering that all students learn equally, regardless of the level of knowledge or skills that they can have.…”

Section: Introductionmentioning

confidence: 99%

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Proposal Models for Personalization of e-Learning based on Flow Theory and Artificial Intelligence

Flores

Alfaro²,

Herrera³

et al. 2019

IJACSA

Self Cite

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This paper presents the comparison of the results of two models for the personalization of learning resources sequences in a Massive Online Open Course (MOOC). The compared models are very similar and differ just in the way how they recommend the learning resource sequences to each participant of the MOOC. In the first model, Case Based Reasoning (CBR) and Euclidean distance is used to recommend learning resource sequences that were successful in the past, while in the second model, the Q-Learning algorithm of Reinforcement Learning is used to recommend optimal learning resource sequences. The design of the learning resources is based on the flow theory considering dimensions as knowledge level of the student versus complexity level of the learning resource with the aim of avoiding the problems of anxiety or boredom during the learning process of the MOOC.

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Section: A Academic Resultsmentioning

confidence: 87%

Section: A Academic Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Proposal Models for Personalization of e-Learning based on Flow Theory and Artificial Intelligence

Flores

Alfaro²,

Herrera³

et al. 2019

IJACSA

Self Cite

View full text Add to dashboard Cite

show abstract

“…Case-based reasoning (CBR), is a paradigm for solving problems by utilizing the knowledge of past cases to solve new cases. Past cases show situations that were previously experienced and that have been stored and studied, so that when there are new cases can be resolved with experience of past cases that have been stored [3].…”

Section: A Case Based Reasoning (Cbr)mentioning

confidence: 99%

Learning Level Prediction System Based on Student Academic Ability By Looking at the Highest Similarity Value

Rahman¹,

Pujianto²

2022

INTECH

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There is a diversity of academic abilities of students in each Indonesian educational institution, each student has different learning abilities according to the level of learning. Educators in providing learning level predictions are done manually, so it takes a long time in predicting student learning levels. In this study, it was able to predict students' academic abilities in an easy and fast way. The method used in predicting learning levels is Case Based Reasoning. This method is able to predict the student's learning level to be (1) Very Bad, (2) Bad, (3) Medium, (4) Good, and (5) Very Good. This level of learning will be used as a benchmark for educators to provide appropriate values to students. The results of this study for the academic performance of the very poor category are 0 students, the bad category is 3 students, the medium category is 79 students, the good category is 16 students and the excellent category is 12 students. The accuracy of academic performance recommendations using confusion matrix is 91.82%.

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“…Adaptive Intelligent Web-Based Systems (AIWBES) or adaptive hypermedia, are an alternative to the traditional approach that only provides the development of web-based educational courses. These systems offer a high degree of adaptability in terms of objectives, preferences, learning styles [32] [33] [34], and individual student knowledge during interaction with the system [35] [36] [37].…”

Section: A Intelligent Adaptive Educational Systemsmentioning

confidence: 99%

A Review of Intelligent Tutorial Systems in Computer and Web based Education

Alfaro

Rivera

Castaneda

et al. 2020

IJACSA

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ITS (Intelligent Tutoring Systems) are integrated and complex systems, designed and developed using approaches and methods of artificial intelligence (AI), for the resolution of problems and requirements of the teaching/learning activities in the field of education and training of students and the workforce based in computers an web based emerging resources. These systems can establish the level of student knowledge and the learning strategies used to improve the level of knowledge to support the detection and correction of student misconceptions. Their purpose is to contribute to the process of teaching and learning in a given area of knowledge, respecting the individuality of the student. In this paper, a review of intelligent tutorial systems (ITS) is presented, from the perspective of their application and usability in modern learning concepts. The methodology used was that of bibliographical review of classic works of the printed and digital literature in relation to ITS and e-Learning systems, as well as searches in diverse databases, of theses and works in universities and digital repositories. The main weakness of the research lies in the fact that the search was limited to documents published in the English, Spanish and Portuguese.

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Proposal model for e-learning based on Case Based Reasoning and Reinforcement Learning

Cited by 4 publications

References 9 publications

Proposal Models for Personalization of e-Learning based on Flow Theory and Artificial Intelligence

Proposal Models for Personalization of e-Learning based on Flow Theory and Artificial Intelligence

Learning Level Prediction System Based on Student Academic Ability By Looking at the Highest Similarity Value

A Review of Intelligent Tutorial Systems in Computer and Web based Education

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