Survey of Technology-Enhanced Learning: Novel Pedagogical Concepts, Challenges and Future Perspectives

Kaur, Tarandeep; Kaur, Shubhpreet

doi:10.1007/978-3-031-18344-7_6

Cited by 2 publications

(2 citation statements)

References 20 publications

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“…When it comes to educational technologies to enhance teaching and learning, educational technologies are the field of study that looks into the process of analyzing, designing, developing, implementing, and evaluating the instructional environment, learning materials, students, and the learning process [32]. Likewise, the role of educational technology in education is crucial because it enables teachers to incorporate new tools and technologies into their classrooms and learners to grab the knowledge at their pace, speed, and time [19]. However, learning is not limited to solo teaching and learning processes but can be extended beyond classroom boundaries [17].…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of E-Learning Courses

Dahal

Pant

Luitel

et al. 2023

Int. J. Interact. Mob. Technol.

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This study attempts to develop a reliable, practical, and efficient process for developing online and distance courses. The study aims to develop e-learning online and distance education courses. ICT and e-Research course development processes have shown significant learning opportunities and outcomes. A substitute for raising these opportunities is the need for innovative research in education while implementing online and distance modes of learning and teaching. Subscribing research and development research methodology, the study aims to produce e-learning instructional materials using a three-stage development model. The course, along with the learning strategy, the learning tools, the assessment tools, and other components, are designed and developed using the Plomp model, a development research model. The research participants were forty-two 2021 Winter Batch MPhil in Educational Studies students and nine lecturers at Nepal Open University (NOU), Nepal. The descriptive analysis uses data analysis to explain the online course's validity, applicability, and effectiveness. Experimental research design was developed using the one-group pre-test post-test design methodology. The most important results are results of preliminary analysis, validity: average material validation is 0.885; average media validation is 0.885 and practicality: more than 71% (very practical). This article proposes a more comprehensive framework to design, develop, and implement online and distance courses in e-learning systems in higher education.

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Section: Introductionmentioning

confidence: 99%

Development and Evaluation of E-Learning Courses

Dahal

Pant

Luitel

et al. 2023

Int. J. Interact. Mob. Technol.

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“…In the conventional educational system [23], teachers encourage students to participate in their learning activities using a variety of methods, such as by having them pay attention, take notes, ask questions, and actively participate in class activities. Nowadays, with the introduction of technology-enhanced learning systems in the classroom [24], engagement tactics for learning are evolving. Therefore, increasing student engagement in onlinelearning environments [25] is still an open research subject that needs to be investigated.…”

Section: Introductionmentioning

confidence: 99%

Student-Engagement Detection in Classroom Using Machine Learning Algorithm

Alruwais

Zakariah

2023

Electronics

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Student engagement is a flexible, complicated concept that includes behavioural, emotional, and cognitive involvement. In order for the instructor to understand how the student interacts with the various activities in the classroom, it is essential to predict their participation. The current work aims to identify the best algorithm for predicting student engagement in the classroom. In this paper, we gathered data from VLE and prepared them using a variety of data preprocessing techniques, including the elimination of missing values, normalization, encoding, and identification of outliers. On our data, we ran a number of machine learning (ML) classification algorithms, and we assessed each one using cross-validation methods and many helpful indicators. The performance of the model is evaluated with metrics like accuracy, precision, recall, and AUC scores. The results show that the CATBoost model is having higher accuracy than the rest. This proposed model outperformed in all the aspects compared to previous research. The results part of this paper indicates that the CATBoost model had an accuracy of approximately 92.23%, a precision of 94.40%, a recall of 100%, and an AUC score of 0.9624. The XGBoost predictive model, the random forest model, and the multilayer perceptron model all demonstrated approximately the same performance overall. We compared the AISAR model with Our model achieved an accuracy of 94.64% compared with AISAR 91% model and it concludes that our results are better. The AISAR model had only around 50% recall compared to our models, which had around 92%. This shows that our models return more relevant results, i.e., if our models predict that a student has high engagement, they are correct 94.64% of the time.

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Survey of Technology-Enhanced Learning: Novel Pedagogical Concepts, Challenges and Future Perspectives

Cited by 2 publications

References 20 publications

Development and Evaluation of E-Learning Courses

Development and Evaluation of E-Learning Courses

Student-Engagement Detection in Classroom Using Machine Learning Algorithm

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