Effects Of Reciprocal Peer Tutoring On Student Performance In An Environmental Control Systems Course At An Undergraduate Level

Choudhury, Ifte

doi:10.18260/1-2--9165

Cited by 4 publications

(4 citation statements)

References 3 publications

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“…RPT is a form of collaborative learning that involves students of similar academic background experience interchanging roles of the tutor and tutee (Gazula et al, 2017). By and large, studies report mixed results as it relates to the benefits of RPT when it comes to student motivation, interest, self-efficacy, test anxiety, and academic achievement (Cheng & Ku, 2009;Choudhury, 2002;Fantuzzo et al, 1990;Griffin & Griffin, 1998;Mickelson et al, 2003;Rittschof & Griffin, 2001). RPT studies have generally been conducted outside the context of computer-supported group formations.…”

Section: Peer Tutoringmentioning

confidence: 99%

Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Lechuga

Doroudi

2022

Int J Artif Intell Educ

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Computer-assisted instructional programs such as intelligent tutoring systems are often used to support blended learning practices in K-12 education, as they aim to meet individual student needs with personalized instruction. While these systems have been shown to be effective under certain conditions, they can be difficult to integrate into pedagogical practices. In this paper, we introduce three group formation algorithms that leverage learning data from the adaptive intelligent tutoring system ALEKS to support pedagogical and collaborative learning practices with ALEKS. Each grouping method was devised for different use cases, but they all utilize a fine-grained multidimensional view of student ability measured across several hundred skills in an academic course. As such, the grouping algorithms not only identify groups of students, but they also determine what areas of ALEKS content each group should focus on. We then evaluate each of the three methods against two alternative baseline methods, which were chosen for their plausibility of being used in practice—one that groups students randomly and one that groups students based on a unidimensional course score. To evaluate these methods, we establish a set of practical metrics based on what we anticipate teachers would care about in practice. Evaluations were performed by simulating mock groupings of students at different time periods for real ALEKS algebra classes that occurred between 2017 and 2019. We show that each devised method obtains more favorable results on the specified metrics than the alternative methods under each use-case. Moreover, we highlight examples where our methods lead to more nuanced groupings than grouping based on a unidimensional measure of ability.

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Section: Peer Tutoringmentioning

confidence: 99%

Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Lechuga

Doroudi

2022

Int J Artif Intell Educ

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“…This can be used to assign students to different school types and academic positions within the school. Choudhury [6] evaluated students' knowledge levels by establishing correlations in the students' performances in the environmental control system course during the undergraduate period. The results prove that there is a positive correlation between students' knowledge level and academic performance.…”

Section: Introductionmentioning

confidence: 99%

Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning

Wang,

et al. 2024

Applied Sciences

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By analyzing students’ understanding of a certain subject’s knowledge and learning process, and evaluating their learning level, we can formulate students’ learning plans and teachers’ curricula. However, the large amount of data processing consumes a lot of manpower and time resources, which increases the burden on educators. Therefore, this study aims to use a machine learning model to build a model to evaluate students’ learning levels for art education. To improve the prediction accuracy of the model, SVM was adopted as the basic model in this study, and was combined with SSA, ISSA, and KPCA-ISSA algorithms in turn to form a composite model. Through the experimental analysis of prediction accuracy, we found that the prediction accuracy of the KPCA-ISSA-SVMM model reached the highest, at 96.7213%, while that of the SVM model was only 91.8033%. Moreover, by putting the prediction results of the four models into the confusion matrix, it can be found that with an increase in the complexity of the composite model, the probability of classification errors in model prediction gradually decreases. It can be seen from the importance experiment that the students’ achievements in target subjects (PEG) have the greatest influence on the model prediction effect, and the importance score is 9.5958. Therefore, we should pay more attention to this characteristic value when evaluating students’ learning levels.

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“…Additionally, peer tutoring has been shown to positively impact self-confidence, student retention, and interest in the subject 1 . The reason for these benefits is attributed to the fact that cooperative learning encourages a higher level of reasoning in which students are more successful in generating new ideas and solutions than during individualistic thinking 2 .…”

Section: Introductionmentioning

confidence: 99%

A Peer-tutor's Perspective on Peer-tutoring in Thermodynamics

Hoff¹,

Davis²,

Hoff³

2012 ASEE Annual Conference &Amp; Exposition Proceedings

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The purpose of this study was to examine the effects of peer tutoring on student performance in a Mechanical Engineering Thermodynamics course and to provide insight into the peer tutoring experience from a variety of perspectives. A Senior-level undergraduate student was recruited to host weekly group study sessions in addition to daily open tutoring hours. This tutor worked with two of the Mechanical Engineering professors who were teaching the Thermodynamics courses over several terms in order to discuss the performance of the students and other general observations regarding the learning experience. Students were surveyed as to their perspective and grades were analyzed at the end of each term in order to quantize the results that were observed. What was found indicates that students who attended the weekly study sessions tended to have a greater comprehension of the material and confidence in their abilities.

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Effects Of Reciprocal Peer Tutoring On Student Performance In An Environmental Control Systems Course At An Undergraduate Level

Cited by 4 publications

References 3 publications

Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Three Algorithms for Grouping Students: A Bridge Between Personalized Tutoring System Data and Classroom Pedagogy

Toward a Comprehensive Evaluation of Student Knowledge Assessment for Art Education: A Hybrid Approach by Data Mining and Machine Learning

A Peer-tutor's Perspective on Peer-tutoring in Thermodynamics

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