Al-Azazi Fatima Ahmed, Ghurab Mossa
Information Technology Department, University of Science and Technology, Sana'a, Yemen.
Computer Science Department, Sana'a University, Sana'a, Yemen.
Heliyon. 2023 Apr 7;9(4):e15382. doi: 10.1016/j.heliyon.2023.e15382. eCollection 2023 Apr.
Learning Analytics aims to discover the class of students' performance over time. This helps instructors make in-time interventions but, discovering the students' performance class in virtual learning environments consider a challenge due to distance constraints. Many studies, which applied to Massive Open Online Courses (MOOC) datasets, built predictive models but, these models were applied to specific courses and students and classify students into binary classes. Moreover, their results were obtained at the end of the course period thus delaying making in-time interventions. To bridge this gap, this study proposes a day-wise multi-class model to predict students' performance using Artificial Neural Network and Long Short-Term Memory, named ANN-LSTM. To check the validity of this model, two baseline models, the Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU), were conducted and compared with ANN-LSTM in this context. Additionally, the results of ANN-LSTM were compared with the state-of-the-art models in terms of accuracy. The results show that the ANN-LSTM model obtained the best results among baseline models. The accuracy obtained by ANN-LSTM was about 70% at the end of the third month of the course and outperforms RNN and GRU models which obtained 53% and 57%, respectively. Also, the ANN-LSTM model obtained the best accuracy results with enhancement rates of about 6-14% when compared with state-of-the-art models. This highlights the ability of LSTM as a time series model to make early predictions for student performance in MOOC taking benefit of its architecture and ability to keep latent dependencies.
学习分析旨在发现学生在一段时间内的成绩类别。这有助于教师进行及时干预，但是，由于距离限制，在虚拟学习环境中发现学生的成绩类别是一项挑战。许多应用于大规模开放在线课程（MOOC）数据集的研究构建了预测模型，但是，这些模型应用于特定课程和学生，并将学生分为二元类别。此外，他们的结果是在课程结束时获得的，因此延迟了及时干预。为了弥补这一差距，本研究提出了一种逐日多类别模型，使用人工神经网络和长短期记忆来预测学生的成绩，名为ANN-LSTM。为了检验该模型的有效性，在此背景下进行了两个基线模型，即递归神经网络（RNN）和门控递归单元（GRU），并与ANN-LSTM进行了比较。此外，还将ANN-LSTM的结果与最先进的模型在准确性方面进行了比较。结果表明，ANN-LSTM模型在基线模型中取得了最佳结果。在课程第三个月末，ANN-LSTM获得的准确率约为70%，优于分别获得53%和57%的RNN和GRU模型。此外，与最先进的模型相比，ANN-LSTM模型获得了最佳的准确率结果，提高率约为6-14%。这突出了LSTM作为时间序列模型利用其架构和保持潜在依赖关系的能力对MOOC中学生成绩进行早期预测的能力。
