Assistive Learning Intelligence Navigator (ALIN) Dataset: Predicting Test Results from Learning Data

Abstract

Data mining techniques have garnered significant attention within the realm of education. In this paper, we present two public available datasets for adaptive learning and studied predicting algorithms for learning results. First, 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 7933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. Second, we proposed methods for predicting the testing results with and without practice tests. Novel learning features are constructed and various machine learning algorithms are compared. Finally, in our experiments, we achieved precision rate of 0.703 and recall rate of 0.734 in the prediction of students’ test outcomes based on behavioral learning data. The robustness of our dataset makes it well-suited for examining the connection between student behavior and academic performance, developing tailored learning recommendations, and exploring diverse research avenues.

Received: 8 September 2023 | Revised: 27 November 2023 | Accepted: 25 December 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest to this work.

Data Availability Statement

The data that support the findings of this study are openly available in [Google] at https://sites.google.com/site/assistmentsdata/home/2009-2010-assistment-data, in [Github] at https://github.com/AdaptiveLearning2022/DataSetALIN2022, and in [IEEE Dataport] at https://ieee-dataport.org/documents/alin-open-dataset-math-adaptive-learning.