He, G., Huang, C., Yang, S., Lwin, K., Ouh, E.L., Ju, R., & Zhu, X. (2023). Assistive Learning Intelligence Navigator (ALIN) Dataset: Predicting Test Results from Learning Data. Preprints. https://doi.org/10.20944/preprints202310.1988.v1
Chicago/Turabian Style
He, G., Ran Ju and Xiaoming Zhu. 2023 "Assistive Learning Intelligence Navigator (ALIN) Dataset: Predicting Test Results from Learning Data" Preprints. https://doi.org/10.20944/preprints202310.1988.v1
Abstract
Data mining techniques have garnered significant attention within the realm of education. However, the procurement of ample student data poses a formidable challenge. In response to this challenge, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7,933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. In our experiments, we achieved successful predictions of students' test outcomes based on behavioral learning data. The strengths of our dataset render it apt for analyzing the nexus between student conduct and academic performance, crafting personalized learning recommendations, and pursuing various other research pursuits.
Computer Science and Mathematics, Computer Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
