Algorithms for the Development of Deep Learning Models for Classification and Prediction of Learner Behaviour in MOOCs

Fotso, José Edmond Meku; Batchakui, Bernabé; Nkambou, Roger; Okereke, George

doi:10.1007/978-3-030-92245-0_3

Cited by 4 publications

(4 citation statements)

References 35 publications

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“…The prediction of student performance and their dropout rate was assessed in a recent study [32] based on students' learning behaviour during online sessions. This work aimed to provide learners and instructors with valuable insights into learner behavior, allowing for a better understanding of the learning process.…”

Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Framework of Deep Learning Techniques to Predict Online Performance of Learners during COVID-19 Pandemic

Altaf,

Asad,

Ahmad

et al. 2023

Sustainability

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COVID-19’s rapid spread has disrupted educational initiatives. Schools worldwide have been implementing more possibilities for distance learning because of the worldwide epidemic of the COVID-19 virus, and Pakistan is no exception. However, this has resulted in several problems for students, including reduced access to technology, apathy, and unstable internet connections. It has become more challenging due to the rapid change to evaluate students’ academic development in a remote setting. A hybrid deep learning approach has been presented to evaluate the effectiveness of online education in Pakistan’s fight against the COVID-19 epidemic. Through the use of multiple data sources, including the demographics of students, online activity, learning patterns, and assessment results, this study seeks to realize the goal of precision education. The proposed research makes use of a dataset of Pakistani learners that was compiled during the COVID-19 pandemic. To properly assess the complex and heterogeneous data associated with online learning, the proposed framework employs several deep learning techniques, including 1D Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. With the 98.8% accuracy rate for the trained model, it was clear that the deep learning framework could beat the performance of any other models currently in use. It has improved student performance assessment, which can inform tailored learning interventions and improve Pakistan’s online education. Finally, we compare the findings of this study to those of other, more established studies on evaluating student progress toward educational precision.

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Section: Literature Reviewmentioning

confidence: 99%

A Hybrid Framework of Deep Learning Techniques to Predict Online Performance of Learners during COVID-19 Pandemic

Altaf,

Asad,

Ahmad

et al. 2023

Sustainability

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“…Due to large amounts of data produced, teachers are using MOOCs to personalise learning, recommend learning paths and detect possible dropouts, thus improving course completion rates [54]. These guide teachers in providing personalised education and supporting interventions that stimulate and enhance the learning process [37]. The algorithms assist in determining learner participation and interaction during the learning process.…”

Section: Use Of Algorithms To Enhance Learningmentioning

confidence: 99%

“…They developed Learner Behaviour Analytics (LBA), an AI-based model to identify and ensure that learners do not cheat during assessments [35]. Fotso et al [37] developed a Recurrent Neural Network (RNN) model that could classify and predict learner behaviour and participation in a MOOC. This model can be extended to identify and track learners and is useful when conducting assessments.…”

Section: Enforcing Credibility Of Assessments By Identifying and Trac...mentioning

confidence: 99%

Opportunities and challenges of adopting MOOCs in Africa: A systematic literature review

Maphosa¹,

Maphosa²

2023

Massive Open Online Courses - Current Practice and Future Trends

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Despite MOOCs' transformative and disruptive potential, African higher education institutions have yet to realise this potential, even though the continent has the highest needs. MOOCs are one of the 21st century's most transformative tools for higher education and offer scalable affordances, increase access to quality education, and offer new learning opportunities to communities previously disadvantaged by location and time. The outbreak of COVID-19 has magnified the need for remote-based teaching to minimise learning disruptions. Although developed countries have adopted MOOCs as a novel tool for technology-enhanced learning, African learners need help adopting such platforms. Literature indexed by the two leading bibliometric databases, the Web of Science and Scopus, were used for a systematic literature review to evaluate research themes on the opportunities and challenges faced when adopting MOOCs in Africa. Using the eight-step guide to conduct a systematic literature review methodology, 36 articles published between 2013 and 2021 were included in the final analysis based on the set criteria. Results indicate that only some African countries are researching MOOCs. The research themes identified regarding opportunities for MOOCs are repurposing MOOCs, democratising access to higher education, professional development of teachers, transitions in the workplace and using algorithms to enhance learning. The research themes identified regarding the challenges when implementing MOOCs are access to the Internet and educational equipment, pedagogical barriers and MOOCs as a preserve of the Global North. MOOCs provide the potential for universal access to education if African governments could enact policies that support development, adoption and growth.

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“…As contrasted to the baseline approaches, the suggested method was more efficient. Fotso et al [23] designed a deep learning approach to estimate instructional practices (learner interactions) within the learning experience, allowing students as well as course faculty members to gain a piece of information about how people acquire knowledge. e authors had utilized relevant information from the UNESCO-IICBA (International Institute for Capacity Building in Africa) MOOC framework, which was constructed for the professional development in Africa, to conduct the assessment.…”

Section: Related Workmentioning

confidence: 99%

Integrating LA and EDM for Improving Students Success in Higher Education Using FCN Algorithm

Hooda

Rana

Dahiya

et al. 2022

Mathematical Problems in Engineering

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EDM and LA are two fields that study how to use facts to get more academic learning and enhance the students’ entire performance. Both areas are concerned with a broad range of issues such as curriculum strategies, coaching, mental well-being of students, learning motivation, and academic achievement. The COVID-19 pandemic highly disrupted the higher education sector and shifted the old, chalk-talk teaching-learning model to an online learning format. This meant that the structure and nature of teaching, learning, assessment, and feedback methodologies also changes. With the empowerment in technology, timely and effective feedback is provided by the teachers to achieve greater learning. Through these studies, it is noted that negative feedback discourages the effort and achievement of learners, so it should be carefully crafted and delivered. In this work, a new methodology is planned based on an improved FCN (fully connected network). The key impartial of the proposed method is to regulate the assessment of the quality of students in Higher Education HE. The proposed methodology is composed of different phases: The first phase is data acquisition, in which the data are gathered from various sources for training and testing of the proposed method. The second phase is data orientation, in which the information is oriented in a specific file format. After that, data are cleaned, and preprocessing methods are applied. In the fourth phase, a machine learning-based model is developed to predict student’s academic performance. The fully connected neural network is enhanced with LA to better assess student quality in higher education. The proposed work is evaluated with the OULAD database, which was gathered from the students of Open University. The proposed methodology has attained an accuracy of 84%, more significant than the conventional ANN model accuracy rate. The proposed methodology’s Recall, F1-score, and precision rates are 0.88, 0.91, and 0.93, respectively.

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Algorithms for the Development of Deep Learning Models for Classification and Prediction of Learner Behaviour in MOOCs

Cited by 4 publications

References 35 publications

A Hybrid Framework of Deep Learning Techniques to Predict Online Performance of Learners during COVID-19 Pandemic

A Hybrid Framework of Deep Learning Techniques to Predict Online Performance of Learners during COVID-19 Pandemic

Opportunities and challenges of adopting MOOCs in Africa: A systematic literature review

Integrating LA and EDM for Improving Students Success in Higher Education Using FCN Algorithm

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