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Abstract
Although online learning platforms are gradually becoming commonplace in modern society, learners’ high dropout rates and serious academic performance require more attention within the virtual learning environment (VLE). This study aims to predict students’ performance in a specific course as it is continuously running, using the statistic personal biographical information and sequential behavior data with VLE. To achieve this goal, a novel recurrent neural network (RNN)-gated recurrent unit (GRU) joint neural network is proposed to fit both static and sequential data, where the data completion mechanism is also adopted to fill the missing stream data. To incorporate the sequential relationship of learning data, three kinds of time-series deep neural network algorithms: simple RNN, GRU, and LSTM are first taken into consideration as baseline models. Their performances are compared in identifying at-risk students. Experimental results on Open University Learning Analytics Dataset (OULAD) show that simple methods like GRU and simple RNN have better results than the relatively complex LSTM model. The results also reveal that different models have different peak performance time, which results in the proposed joint model that achieves over 80% prediction accuracy of at-risk students at the end of the semester.
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Progressive Teaching Improvement For Small Scale Learning: A Case Study in China. FUTURE INTERNET 2020. [DOI: 10.3390/fi12080137] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term.
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Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment. SUSTAINABILITY 2019. [DOI: 10.3390/su11247238] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
In higher education, predicting the academic performance of students is associated with formulating optimal educational policies that vehemently impact economic and financial development. In online educational platforms, the captured clickstream information of students can be exploited in ascertaining their performance. In the current study, the time-series sequential classification problem of students’ performance prediction is explored by deploying a deep long short-term memory (LSTM) model using the freely accessible Open University Learning Analytics dataset. In the pass/fail classification job, the deployed LSTM model outperformed the state-of-the-art approaches with 93.46% precision and 75.79% recall. Encouragingly, our model superseded the baseline logistic regression and artificial neural networks by 18.48% and 12.31%, respectively, with 95.23% learning accuracy. We demonstrated that the clickstream data generated due to the students’ interaction with the online learning platforms can be evaluated at a week-wise granularity to improve the early prediction of at-risk students. Interestingly, our model can predict pass/fail class with around 90% accuracy within the first 10 weeks of student interaction in a virtual learning environment (VLE). A contribution of our research is an informed approach to advanced higher education decision-making towards sustainable education. It is a bold effort for student-centric policies, promoting the trust and the loyalty of students in courses and programs.
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Abstract
Research and development activities are one of the main drivers for progress, economic growth and wellbeing in many societies. This article proposes a text mining approach applied to a large amount of data extracted from job vacancies advertisements, aiming to shed light on the main skills and demands that characterize first stage research positions in Europe. Results show that data handling and processing skills are essential for early career researchers, irrespective of their research field. Also, as many analyzed first stage research positions are connected to universities, they include teaching activities to a great extent. Management of time, risks, projects, and resources plays an important part in the job requirements included in the analyzed advertisements. Such information is relevant not only for early career researchers who perform job selection taking into account the match of possessed skills with the required ones, but also for educational institutions that are responsible for skills development of the future R&D professionals.
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