Activating and Engaging Students in Online Asynchronous Classes

Libre, Nicolas Ali

doi:10.18260/1-2--36643

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…A study published in Ref. (Libre, 2021) found that there was a dearth of interaction during online learning between students and instructors or between students themselves. By creating interactive and engaging classrooms, students can actively participate, think critically, collaborate, and navigate digital platforms effectively.…”

Section: Introductionmentioning

confidence: 99%

Implementing Book-end Division Approach using ClassPoint to Energize Electrical and Electronics Engineering Student Engagement

Arshad,

Bakar,

Hasan

et al. 2023

AJEE

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TThis study investigates the efficacy of using ClassPoint in improving student engagement during class and its impact on academic performance among electrical and electronics engineering students by using student engagement framework that established by technology-enhanced learning (TEL) environment microsystem. To achieve this objective, five instructors teaching various courses incorporated ClassPoint into their classes. Then, quantitative data on student engagement and academic performance were collected via surveys. The student’s comments are extracted from university teaching evaluation survey (EPAT) for thematic analysis. The descriptive analysis revealed a significant increase in student engagement after ClassPoint was implemented. Furthermore, students appreciated the use of ClassPoint features such as slide-drawing, multiple choice questions, and word clouds during classes. Survey results also show students have greater attentiveness, active participation, and improved interactions with their peers and instructors. Likert scale responses indicate positive correlation between the use of ClassPoint and students’ enhanced performance in class discussions, idea integration, increased interest in learning, and improved classroom dynamics. Moreover, thematic analysis shows the empowering of five element in TEL microsystem with ClassPoint increase the student engagement. This study highlights ClassPoint's effectiveness in creating an inclusive and interactive learning environment, thus, transforming teaching methods for electrical and electronics engineering students.

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Section: Introductionmentioning

confidence: 99%

Implementing Book-end Division Approach using ClassPoint to Energize Electrical and Electronics Engineering Student Engagement

Arshad,

Bakar,

Hasan

et al. 2023

AJEE

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“…The failure rate in the medium risk and low-risk zones were 36% and 16%, respectively. While the course learning objectives were kept the same, the course delivery format and the interactive activities have changed significantly when the course was offered in the online format [10]. Besides the changes imposed by the COVID restrictions, many active learning activities were incorporated into the course.…”

Section: Accuracy In Identifying At-risk Studentsmentioning

confidence: 99%

Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics

Libre

2022 ASEE Annual Conference &Amp; Exposition Proceedings

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The ability to predict student performance and identify those who are "academically at-risk" creates opportunities to improve educational outcomes and enhance retention. This research aimed at developing and implementing a predictive model to generate an alert of potential student failure in engineering mechanics courses. A prediction model is developed based on regression analysis and is used to predict students' final course grades based on their activities early in the semester. In general, a predictive model needs a refined and standardized data set in order to achieve meaningful results. The data set used for the grade prediction includes students' performance in the formative and summative assessments during the first four weeks of the semester as well as their class participation and homework completion. The training data was collected in two semesters where the course was offered in-person in large sections with more than 100 students per section; then the model was tested and validated in small and large sections in four consecutive semesters, two of which involved online teaching mode. The analysis of all collected data shows that the accuracy of the model is 88.0%. In particular, the model successfully predicted students who were in danger of failure. The accuracy of prediction was acceptable in both in-person and online teaching modes. The ultimate goal of developing and implementing the model is utilizing a reliable tool that is capable of predicting student performance with an acceptable accuracy to identify "academically at-risk" students. The intervention process is beyond the scope of this paper and will be discussed in a separate publication. The results of this study will provide all parties involved in student success (e.g. instructors, advisors, counselors, etc.) with a tool to identify at-risk students early in the semester enabling proactive intervention approaches before it is too late, to increase student success and retention.

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Activating and Engaging Students in Online Asynchronous Classes

Cited by 2 publications

References 11 publications

Implementing Book-end Division Approach using ClassPoint to Energize Electrical and Electronics Engineering Student Engagement

Implementing Book-end Division Approach using ClassPoint to Energize Electrical and Electronics Engineering Student Engagement

Grade Prediction Model Using Regression Analysis: an Implementation in Engineering Mechanics

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