Correlation Analysis to Quantify Learning Outcomes for Different Teaching Pedagogies
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Correlation Analysis to Quantify Learning Outcomes for Different Teaching Pedagogies

Authors: Kanika Sood, Sijie Shang

Abstract:

A fundamental goal of education includes preparing students to become a part of the global workforce by making beneficial contributions to society. In this paper, we analyze student performance for multiple courses that involve different teaching pedagogies: a cooperative learning technique and an inquiry-based learning strategy. Student performance includes student engagement, grades, and attendance records. We perform this study in the Computer Science department for online and in-person courses for 450 students. We will perform correlation analysis to study the relationship between student scores and other parameters such as gender, mode of learning. We use natural language processing and machine learning to analyze student feedback data and performance data. We assess the learning outcomes of two teaching pedagogies for undergraduate and graduate courses to showcase the impact of pedagogical adoption and learning outcome as determinants of academic achievement. Early findings suggest that when using the specified pedagogies, students become experts on their topics and illustrate enhanced engagement with peers.

Keywords: Bag-of-words, cooperative learning, education, inquiry-based learning, in-person learning, Natural Language Processing, online learning, sentiment analysis, teaching pedagogy.

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References:


[1] M. Hannay, Perceptions of distance learning: A comparison of online and traditional learning,2006
[2] L. W. Perna, Preparing today’s students for tomorrow’s jobs in metropolitan America, 2013
[3] E. Aronson, The jigsaw classroom, 1978
[4] Z. Iqbal, J. Qadir, A. N. Mian, F. Kamiran, Machine learning based student grade prediction: A case study, 2017
[5] C. Leung, S. Chan, K. F. Chung, Integrating collaborative filtering and sentiment analysis: A rating inference approach, 2006
[6] S. Loria, Textblob Documentation, 2018
[7] T.Byers, W. Imms, E. Hartnell-Young, Comparative analysis of the impact of traditional versus innovative learning environment on student attitudes and learning outcomes, 2018
[8] S. Nonis , G. Fenner, n Exploratory Study of Student Motivations for Taking Online Courses and Learning Outcomes, 2012
[9] H. Boon, B. Lewthwaite, Development of an instrument to measure a facet of quality teaching: Culturally responsive pedagogy, 2015
[10] CSUFRegistration and Records, Grading system, 2022
[11] H. Johnson, M. C. Mejia, Online learning and student outcomes in California’s community colleges, 2014
[12] L. Wallis, Growth in distance learning outpaces total enrollment growth, 2020
[13] D. Lederman, Online enrollments grow, but pace slows, 2019
[14] P. Fain, Takedown of online education, 2019
[15] R. Angiello, Study looks at online learning vs. traditional instruction, 2010