Contrasting prediction methods for early warning systems at undergraduate level

Howard, Emma; Meehan, Maria; Parnell, Andrew

doi:10.1016/j.iheduc.2018.02.001

Cited by 106 publications

(68 citation statements)

References 23 publications

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“…Considering that our interest is to early predict at-risk students, we measured the performances of the models until the middle of the semester (8 weeks). It is possible to say that the models achieved performances that can be considered satisfactory (with AUC ROC values of 90% already in the first week) and it is similar to the results found in the literature, for example, Detoni et al [25], Howard et al [46], Sandoval et al [31], and Lu et al [47]. These results were found considering the pre-processing of the datasets using SMOTE to balance the classes.…”

Section: Discussionsupporting

confidence: 83%

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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: Discussionsupporting

confidence: 83%

“…Aiming to find the optimal time in a course to apply an early warning system, the authors of Howard et al [46] examined eight prediction methods to identify at-risk students. The course has a weekly continuous assessment and a large proportion of resources on the LMS.…”

Section: Early Predictionmentioning

confidence: 99%

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

et al. 2019

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“…However, this study did not include as predictors, assessment results, multiple fine-grained measures of LMS usage or (with the exception of one course which showed a positive correlation) measures of student interaction with one another. Given that these have been shown to be predictive of academic performance previously (Azcona & Casey, 2015;Civitas 2016;Conijn et al, 2016;Howard et al 2018;Macfadyen & Dawson, 2010;Zacharis, 2015), it is likely that the predictive accuracy observed in this study can be improved further.…”

Section: Discussionmentioning

confidence: 55%

“…In this case there is usually a trade-off between time of prediction and accuracy of prediction. Accuracy generally increases over time as more course data becomes available (Howard, Meehan & Parnell, 2018). There have been some exceptions, for example, Sandoval, Gonzalez, Alarcon, Pichara and Montenegro (2018) who found better predictive accuracy for aggregated LMS data earlier in the course.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of student academic performance using Moodle data from a Further Education setting.

Quinn¹,

Gray²

2019

telji

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Increasingly educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A key focus was classifiers that could predict likelihood of failure from data available early in the term. The results showed that classifiers built on all course data predicted student grade moderately well (accuracy= 60.5%, kappa = 0.43) and whether a student would pass or fail very well (accuracy= 92.2%, kappa=0.79). However, classifiers built on the first six weeks of data did not predict failing students well. Classifiers trained on the first ten weeks of data improved significantly on a no-information rate (p<0.008) though more than half of failing students were still misclassified. The evidence indicates that measures of Moodle activity on further education courses could be useful as part of on an early-warning system at ten weeks.

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An ensembling model for early identification of at‐risk students in higher education

Gupta

Garg

Kumar

2021

Comp Applic In Engineering

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Audience Response Systems like clickers are gaining much attention for early identification of at‐risk students as quality education, student success rate and retention are major concerning areas, as evidenced in this COVID scenario. Usage of this active learning strategy across the varying strength of classrooms are found to be much effective in retaining the attention, retention and learning power of the students. However, implementing clickers for large classrooms incur overhead costs on instructor's part. As a result, educational researchers are experimenting with various lightweight alternatives. This paper discusses one such alternative: lightweight formative assessments for blended learning environments. It discusses their implementation and effectiveness in early identification of at‐risk students. This study validates the usage of lightweight assessments for three core pedagogically different courses of large computer science engineering classrooms. It uses voting ensemble classifier for effective predictions. With the usage of lightweight assessments in early identification of at‐risk students, accuracy range of 87%–94.7% have been achieved along‐with high ROC‐AUC values. The study also proposes the generalized pedagogical architecture for fitting in these lightweight assessments within the course curriculum of pedagogically different courses. With the constructive outcomes, the light‐weight assessments seem to be promising for efficient handling of scaling technical classrooms.

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Contrasting prediction methods for early warning systems at undergraduate level

Cited by 106 publications

References 23 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

Prediction of student academic performance using Moodle data from a Further Education setting.

An ensembling model for early identification of at‐risk students in higher education

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