Predicting high-risk students using Internet access logs

Zhou, Qing; Quan, Wenjun; Zhong, Yiren; Wei, Xiao; Mou, Chao; Wang, Yong

doi:10.1007/s10115-017-1086-5

Cited by 23 publications

(22 citation statements)

References 36 publications

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“…Three machine learning methods were tested and results showed that the patterns in the data could provide useful information to classify at-risk students, allowing personalized activities, trying to avoid the student dropout. In Zhou et al [45], the authors created a feature selection framework to pre-processing the data coming from internet access logs and generate models to predict the students' performance. Results have shown that this approach can identify most of the high-risk students.…”

Section: Only Interaction Data/log Filesmentioning

confidence: 99%

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

et al. 2019

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Algorithms and programming are some of the most challenging topics faced by students during undergraduate programs. Dropout and failure rates in courses involving such topics are usually high, which has raised attention towards the development of strategies to attenuate this situation. Machine learning techniques can help in this direction by providing models able to detect at-risk students earlier. Therefore, lecturers, tutors or staff can pedagogically try to mitigate this problem. To early predict at-risk students in introductory programming courses, we present a comparative study aiming to find the best combination of datasets (set of variables) and classification algorithms. The data collected from Moodle was used to generate 13 distinct datasets based on different aspects of student interactions (cognitive presence, social presence and teaching presence) inside the virtual environment. Results show there are no statistically significant difference among models generated from the different datasets and that the counts of interactions together with derived attributes are sufficient for the task. The performances of the models varied for each semester, with the best of them able to detect students at-risk in the first week of the course with AUC ROC from 0.7 to 0.9. Moreover, the use of SMOTE to balance the datasets did not improve the performance of the models.

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Section: Only Interaction Data/log Filesmentioning

confidence: 99%

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

et al. 2019

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“…Several lines of evidence suggest that LMS data can be used to predict student academic performance. Nevertheless, one major drawback of LMS's data is that it can be used to predict students' academic performance in only a particular unit of study, not across all units of study (Zhou et al 2018). Using additional data sources increases the potential to better predict students' performance (Conijn et al 2017).…”

Section: Students' Academic Performance Predictionmentioning

confidence: 99%

“…To discover the factors related to students at-risk of failure, educational data mining can be used to predict students' academic performance (Zhou et al 2018;He et al 2015). Those students at-risk of failure can potentially be identified by using factors inherent in internal university data sources (Amornsinlaphachai 2016;Vuttipittayamongkol 2016;Arsad, Buniyamin, and Manan 2012;Senthil and Lin 2017).…”

Section: Introductionmentioning

confidence: 99%

“…This incurs extra costs to manually collect the data. Although LMSs overcome these problems, its data is normally limited to a single unit of study and overall academic performance cannot be generalised from LMS data across all units of study (Zhou et al 2018). Hence, using data such as internet access log files offers a promising alternative to explore the prediction of academic performance of students.…”

Section: Introductionmentioning

confidence: 99%

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A Study of Educational Data Mining: Evidence from a Thai University

Trakunphutthirak

Cheung

Lee

2019

AAAI

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Educational data mining provides a way to predict student academic performance. A psychometric factor like time management is one of the major issues affecting Thai students’ academic performance. Current data sources used to predict students’ performance are limited to the manual collection of data or data from a single unit of study which cannot be generalised to indicate overall academic performance. This study uses an additional data source from a university log file to predict academic performance. It investigates the browsing categories and the Internet access activities of students with respect to their time management during their studies. A single source of data is insufficient to identify those students who are at-risk of failing in their academic studies. Furthermore, there is a paucity of recent empirical studies in this area to provide insights into the relationship between students’ academic performance and their Internet access activities. To contribute to this area of research, we employed two datasets such as web-browsing categories and Internet access activity types to select the best outcomes, and compared different weights in the time and frequency domains. We found that the random forest technique provides the best outcome in these datasets to identify those students who are at-risk of failure. We also found that data from their Internet access activities reveals more accurate outcomes than data from browsing categories alone. The combination of two datasets reveals a better picture of students’ Internet usage and thus identifies students who are academically at-risk of failure. Further work involves collecting more Internet access log file data, analysing it over a longer period and relating the period of data collection with events during the academic year.

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“…Also, click counts on learning materials (Wolff & Zdrahal, 2012;Wolff, Zdrahal, Herrmannova, & Knoth, 2014), the quantity and quality of forum posts, as well as students' social network positions in forums (Romero, Lopez, Luna, & Ventura, 2013) are also influential features of predictive models. Nevertheless, in a more pervasive perspective, Zhou et al (2018) collected all the web access logs of students in a local university to train predictive models, and found that at-risk students tended to spend more time visiting entertainment-related sites than educational sites. The fact that both types of data are useful naturally incurs the practice of combining the power of both.…”

Section: Datamentioning

confidence: 99%

A joint neural network model for combining heterogeneous user data sources: An example of at‐risk student prediction

Qiao¹,

Hu²

2019

Asso for Info Science & Tech

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Information service providers often require evidence from multiple, heterogeneous information sources to better characterize users and offer personalized service. In many cases, statistic information (for example, users' profiles) and sequentially dynamic information (for example, logs of interaction with information systems) are two prominent sources that can be combined to achieve optimized results. Previous attempts in combining these two sources mainly exploited models designed for either static or sequential information, but not both. This study aims to fill the gap by proposing a novel joint neural network model that can naturally fit both static and sequential user data. To evaluate the effectiveness of the proposed method, this study uses the problem of at‐risk student prediction as an example where both static data (personal profiles) and sequential data (event logs) are involved. A thorough evaluation was conducted on an open data set, with comparisons to a range of existing approaches including both static and sequential models. The results reveal superb performances of the proposed method. Implications of the findings on further research and applications of joint models are discussed.

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Predicting high-risk students using Internet access logs

Cited by 23 publications

References 36 publications

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

Predicting Students Success in Blended Learning—Evaluating Different Interactions Inside Learning Management Systems

A Study of Educational Data Mining: Evidence from a Thai University

A joint neural network model for combining heterogeneous user data sources: An example of at‐risk student prediction

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