Inquiry Based Inductive Learning Practices in Engineering Education: A Classroom Study

Jayasingh, Bipin Bihari; Kumar, Hemant; Aishwarya, G.

doi:10.1109/mite.2016.022

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…To forecast pupils' grades, Jayasingh et al [8] utilized a Bayesian classification algorithm that relied on the previous year performance data. Teachers and students alike stand to benefit from the research, according to the findings of the researchers.…”

Section: Literature Surveymentioning

confidence: 99%

Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors

Kamal

Chakrabarti

Ramírez-Asís

et al. 2022

Mathematical Problems in Engineering

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Over the last few decades, there has been a gradual deterioration in higher education in all three areas: the academic setting (both staff and students), as well as research and development output (including graduates). All colleges and universities are essentially focused on improving management decision-making and educating pupils. High-quality higher education can be obtained through a variety of methods. One method is to accurately forecast pupils’ achievement in their chosen educational context. There are numerous prediction models from which to pick. While it is unclear whether there are any markers that can predict whether a kid will be an academic genius, a dropout, or an average performer, the researcher reports student achievement. This article presents a metaheuristics and machine learning-based method for the classification and prediction of student performance. Firstly, features are selected using a relief algorithm. Machine learning classifiers such as BPNN, RF, and NB are used to classify student academic performance data. BPNN is having better accuracy for classification and prediction of student academic performance.

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Section: Literature Surveymentioning

confidence: 99%

Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors

Kamal

Chakrabarti

Ramírez-Asís

et al. 2022

Mathematical Problems in Engineering

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“…JIT learning in this study is also used within the context of many of these other inductive teaching methods, including project-and problem-based learning to more effectively achieve a broad range of learning outcomes [4]. Inductive learning in engineering classrooms is popular and has been shown to improve student motivation and content retention [6,7,8].…”

Section: Introductionmentioning

confidence: 99%

GIFTS: Exploration Activities for Just-in-Time Learning in a First-Year Engineering Robotics Design-Build Project

Vadlamani,

Rumreich,

Phillips

2023 ASEE Annual Conference &Amp; Exposition Proceedings

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This GIFTS paper will provide an example of how Just-in-Time (JIT) learning can be used as a technique in a first-year engineering design-build robotics course to encourage student motivation, learning, and creativity. This paper explores using JIT learning for unique software and hardware skills in a project-based course where students design, build, and program an autonomous robot. Data on student perceptions of the JIT learning activities indicate students find the assignments useful for their learning. This paper will also describe how choice is preserved in student decision-making for an open-ended project while also guaranteeing students learn the desired course learning objectives.

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Machine Learning Techniques for Quality Management in Teaching Learning Process in Higher Education by Predicting the Student's Academic Performance

Meenakshi¹,

Sanchez²,

Peconcillo³

et al. 2022

ijngc

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The process of teaching and learning is the most powerful tool that teachers and professors have, because it is the main way that students change in the ways that teachers and professors want them to. TQM is recommended as a way to control, monitor, and improve the quality of teaching and learning strategies in the classroom. In line with the principles of TQM, evaluations of things like results and feedback are used to improve teaching and learning. The level of academic success that a program's students have may say a lot about how good that programme is. You can predict how well a student will do in school in the future by looking at how well they did in the past. After more research, it might be possible to find a link between the students' grades and their skills and interests. When teachers have this kind of information, they are better able to focus on the students who are having the most trouble. This article shows how to use feature selection and machine learning to improve the quality of teaching and learning in higher education by predicting how well a student will do in school. The performance of university students is used as an input to a classification model. First, ant colony optimization is used to choose the most important features. Then, KNN, Naive Bayes, and decision tree algorithms are used to classify the chosen data. Based on accuracy, recall, and F1 score, KNN performs better.

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Inquiry Based Inductive Learning Practices in Engineering Education: A Classroom Study

Cited by 3 publications

References 14 publications

Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors

Metaheuristics Method for Classification and Prediction of Student Performance Using Machine Learning Predictors

GIFTS: Exploration Activities for Just-in-Time Learning in a First-Year Engineering Robotics Design-Build Project

Machine Learning Techniques for Quality Management in Teaching Learning Process in Higher Education by Predicting the Student's Academic Performance

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