An Adaptive Learning Approach for Better Retention of Learners in MOOCs

Smaili, El Miloud; Sraidi, Soukaina; Azzouzi, Salma; Hassan, Charaf My El

doi:10.1145/3386723.3387845

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…The paper gives a formal representation of these feature elements and a method for initializing and updating the values. Finally, this paper is a continuity of previous works [3,22,23,24,25] where we provide some solutions to enhance adaptive learning systems using some optimization algorithms. Unlike other approaches proposed in the literature, the solution tracks the evolution of learners in real time and updates their profiles accordingly, which presents a serious challenge to existing research on adaptive learning.…”

Section: Literature Reviewmentioning

confidence: 95%

“…Moreover, data mining and learning analytics can support more effective use of recorded learning data to assess learning processes, predict future performance, and identify potential problems. For this purpose, one solution to overcome both the high dropout rate and the heterogeneity of learner profiles may be the use of adaptive learning [3]. As mentioned earlier, MOOCs are a way to serve a large number of learners, and consequently to serve a large heterogeneity of profiles on various online platforms.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Innovative Approach to Prevent Learners’ Dropout from MOOCs using Optimal Personalized Learning Paths: An Online Learning Case Study

SMAILI

Khoudda

Sraidi

et al. 2022

Stat., optim. inf. comput.

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Due to the rapid evolution of information technology, distance education has undergone a sustained expansion. Within the scope of open distant learning, MOOCs (Massive Open Online Courses) tend to open up access to education to all by bridging geographical and economic barriers. However, the evolution of this learning mode is facing some challenges such as the low completion rate of the courses as well as the heterogeneity of the learners’ profifiles. In this paper, we aim to personalize the MOOC contents for each learner in order to improve their academic performance and to enhance the online platforms effificiency. The idea is to build a system that takes into account the heterogeneity of learners profifiles and offers each learner a path adapted to their needs through the exploitation of their interactions with the learning environment. To this end, we suggest using the PSO method ”Particle Swarm Optimization” in order to construct the optimal choices of learning paths in the system. Furthermore, we conduct an online learning case study to show the effectiveness of the proposed approach.

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Section: Literature Reviewmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

An Innovative Approach to Prevent Learners’ Dropout from MOOCs using Optimal Personalized Learning Paths: An Online Learning Case Study

SMAILI

Khoudda

Sraidi

et al. 2022

Stat., optim. inf. comput.

View full text Add to dashboard Cite

show abstract

“…According to Miloud, Soukaina, Salma, and El Hassan (2020), a MOOC's design should be centered on an adaptive online learning system that boosts course completion rates. This will ensure that the suggested course corresponds to the most effective manner for the learner to finish the learning process.…”

Section: The Efficiency Of Adaptive and Intelligent Moocsmentioning

confidence: 99%

Adaptive and Intelligent MOOCs: How they Contribute to the Improvement of the MOOCs’ Effectiveness

Papadimitriou

2023

IJREL

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Several traditional MOOCs have been developed utilizing particular traditional approaches for distance learning. The main objective of this article is to examine numerous studies and research about the provision of adaptive and intelligent MOOCs to address issues, such as dropout rate, for improving their efficiency compared to conventional MOOCs. Important issues that have been the essential study interests of MOOC scholars in recent years, including dropout rate, completion rate, loneliness, and other topics, were studied. Finally, the research questions posed on the effectiveness of Adaptive and Intelligent MOOCs, the learner’s characteristics used for adaptation, the adaptive and intelligent methods and techniques used, and the improvements they bring to traditional MOOCs as a compass for designing Adaptive and Intelligent MOOCs in the coming years, are discussed.

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“…Nowadays, with the awareness of educators to improve the quality of education, many educators have implemented adaptive learning in the teaching and learning process. Therefore, many researchers have developed adaptive learning frameworks as valuable tools to support educators in the teaching and learning process [5]. An adaptive learning framework is a crucial method that utilizes research to organize educational content and activities based on the student's skills and interests.…”

Section: Introductionmentioning

confidence: 99%

Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

Halim¹,

Mohemad²,

Ali³

2023

IJETAE

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Adaptive learning allows students to learn effectively based on their abilities and characteristics at their own pace. Currently, numerous SLRs exist regarding students' adaptive learning characteristics, analyzed from various perspectives by previous researchers. However, it is essential to note that the findings of these studies are based on analyses conducted between 2010 and 2020. This SLR aims to extend and bridge the gaps in identifying essential student characteristics for implementing an adaptive learning system. It specifically focuses on the most recent five-year period, from 2018 to 2022. This study chose 39 articles according to the specified inclusion and exclusion criteria. The findings from the SLR indicate that learning style is the most commonly used element of adaptation in the reviewed articles, followed by knowledge characteristics, cognitive traits, student preference, and motivation. This SLR also revealed that most of the reviewed articles used more than one student's characteristics in modeling the student model, and results show that educators integrated online learning for implementing adaptive learning in the teaching and learning process. The ILS instrument, which is traditional detection, is widely used in collecting learning style data, and besides FSLSM, other learning style models, including VARK, Kolb, and Honey & Mumford, are used to assign students' learning style preferences.

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An Adaptive Learning Approach for Better Retention of Learners in MOOCs

Cited by 7 publications

References 11 publications

An Innovative Approach to Prevent Learners’ Dropout from MOOCs using Optimal Personalized Learning Paths: An Online Learning Case Study

An Innovative Approach to Prevent Learners’ Dropout from MOOCs using Optimal Personalized Learning Paths: An Online Learning Case Study

Adaptive and Intelligent MOOCs: How they Contribute to the Improvement of the MOOCs’ Effectiveness

Identification of Student’s Characteristics in Adaptive Learning System: Systematic Literature Review

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