Providing feedback on computer-based algebra homework in middle-school classrooms

Fyfe, Emily R.

doi:10.1016/j.chb.2016.05.082

Cited by 50 publications

(54 citation statements)

References 27 publications

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“…As described by Fyfe, around the globe, ITS and technology-assisted homework achieved fame and pervasiveness in schools [21]. Conferring to Ma et al, personalized education, and well before advice, is the solid foundation of intelligent tutoring systems [15].…”

Section: Related Workmentioning

confidence: 98%

“…Conferring to Ma et al, personalized education, and well before advice, is the solid foundation of intelligent tutoring systems [15]. The objective of the study by Fyfe was to reveal an investigational assessment of an algebra class for middle school students who had preceding variable knowledge affected by the technology-based responses [21].…”

Section: Related Workmentioning

confidence: 99%

“…Generally, many types of research support the notion that using the in-time responses from ITSs, as usual, has useful properties on learning outputs as opposed to no response from ITSs [20,21]. Lee et al, Baker et al, and Gupta and Rose, all classified that confusion, both its roots and penalties, can be easily recognized through the performance and student actions [22][23][24].…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi

Hussain

et al. 2018

Sustainability

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Incorporating substantial, sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study was to identify the confused students who had failed to master the skill(s) given by the tutors as homework using the Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study, and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models including: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated, and tested learning algorithms, performed stratified cross-validation, and measured the performance of the models through various performance metrics, i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity, and Specificity. We found RF, GLM, XGBoost, and DL were high accuracy-achieving classifiers. However, other perceptions such as detecting unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students that were confused when attempting the homework exercise, to help foster their knowledge and talent to play a vital role in environmental development.

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Section: Related Workmentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi

Hussain

et al. 2018

Sustainability

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“…Although feedback can improve learner's performance, the effectiveness of feedback is determined by various factors. For example, it was established that elaborated feedback does not necessarily improve learner's performance as compared to simple feedback [25]. Learner's prior knowledge and task difficulty are found to moderate the effectiveness of feedback on learning performance [26].…”

Section: Background and Hypothesesmentioning

confidence: 99%

The Effect of Structured Feedback on Performance: the Role of Attitude and Perceived Usefulness

Cheah

2020

Sustainability

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In higher education, university students need to be equipped with the necessary knowledge and skills for their future workplace. Companies provide opportunities for students to participate as trainee consultants in innovation-related projects as a form of experiential learning. We designed a quasi-experimental study to investigate how supervisors’ structured feedback can influence students’ learning and project performance. We recruited 28 business school final year undergraduates who formed teams of four students either by themselves or with the help of the school. We randomly assigned three teams with 12 students to the treatment group and four teams of 16 students to the control group. In the treatment group, company supervisors were asked to provide structured written feedback for the treatment group using structured feedback forms, which focus students’ attention on task level, process level, and self-regulation level at three project milestone stages. In the control group, students received feedback from company supervisors without any feedback form. At the end of this project, a survey was conducted to measure students’ attitudes toward feedback in supporting learning and their perceived usefulness of company supervisors’ feedback. The results show that company supervisors’ structured feedback positively affected students’ overall project performance in presentation and report, and students’ positive attitudes toward feedback and perceived usefulness of company supervisor’s structured feedback are positively related to students’ report performance.

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“…As per Fyfe, around the globe, ITS and technology-assisted homework achieved fame and pervasiveness in schools [18].…”

Section: Related Workmentioning

confidence: 99%

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Abidi¹,

Hussain²,

Xu³

et al. 2018

Preprint

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Incorporating substantial sustainable development issues into teaching and learning is the ultimate task of Education for Sustainable Development (ESD). The purpose of our study is to identify the confused students who have failed to master the skill(s) given by the tutors as a homework using Intelligent Tutoring System (ITS). We have focused ASSISTments, an ITS in this study and scrutinized the skill-builder data using machine learning techniques and methods. We used seven candidate models that include: Naïve Bayes (NB), Generalized Linear Model (GLM), Logistic Regression (LR), Deep Learning (DL), Decision Tree (DT), Random Forest (RF), and Gradient Boosted Trees (XGBoost). We trained, validated and tested learning algorithms, performed stratified cross-validation and measured the performance of the models through various performance metrics i.e., ROC (Receiver Operating Characteristic), Accuracy, Precision, Recall, F-Measure, Sensitivity & Specificity. We found GLM, DT & RF are high accuracies achieving classifiers. However, other perceptions such as detection of unexplored features that might be related to the forecasting of outputs can also boost the accuracy of the prediction model. Through machine learning methods, we identified the group of students which were confused attempting the homework exercise and can help students foster their knowledge, and talent to play a vital role in environmental development.

show abstract

Providing feedback on computer-based algebra homework in middle-school classrooms

Cited by 50 publications

References 27 publications

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

The Effect of Structured Feedback on Performance: the Role of Attitude and Perceived Usefulness

Prediction of Confusion Attempting Algebra Homework in an Intelligent Tutoring System through Machine Learning Techniques for Educational Sustainable Development

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