Using early assessment performance as early warning signs to identify at-risk students in programming courses

Veerasamy, Ashok Kumar; D'Souza, Daryl; Apiola, Mikko-Ville; Laakso, Mikko‐Jussi; Salakoski, Tapio

doi:10.1109/fie44824.2020.9274277

Cited by 16 publications

(12 citation statements)

References 33 publications

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“…However, we can see from the Table that only twenty-one (21) studies addressed this. Lastly, only fourteen (14) of the studies acknowledged that the obtained results were verified using a second dataset, on unseen data other than the training and testing sets ([16], [27], [121], [129], [130], [135], [46], [50], [61], [63], [104], [106], [117], [120]), which may imply the generalizability of results.…”

Section: Quality Assessment Resultsmentioning

confidence: 99%

“…In this step, we analysed the articles based on the quality of the dataset, in terms of the features used, the sample size, population, and collection methods. As can be seen in Figure 4, majority of researchers have used available data sources and records found in public dataset or through university/school repositories, precisely a total of seventy-three (73) studies indicate this ([10], [13], [56], [58]- [66], [20], [69], [71]- [78], [81], [27], [82], [83], [85], [87]- [93], [45], [94]- [97], [100]- [104], [106], [49], [107]- [110], [113]- [117], [119], [50], [121], [123]- [126], [128], [129], [131], [132], [134], [51], [135], [136], [138], [54], [55]). While these sources are easily accessible and provide expected accuracy of the prediction models, some researchers opted for a mixed-mode of data collection where both available sources and que...…”

Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

“…The second highest is IBM SPSS for statistical approaches with 13% ( [13], [66], [70], [82]- [84], [86], [95], [120], [136]). Other most-used tools include Microsoft Excel (17%) ([20], [52], [117], [123], [135], [71], [82]- [84], [86], [89], [112], [115]), RapidMiner Studio (9%) ( [13], [49], [70], [86], [91], [107], [131]), Jupyter Notebook (7%) ( [115], [123], [124], [127], [132]), Orange and R Studio (4%) ( [62], [87], [116], [125], [128], [133] respectively), and Tableau (3%) ( [77], [127]).…”

Section: Prediction Approaches Algorithms Learning Outcome Type and T...mentioning

confidence: 99%

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Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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Student retention is an essential measurement metric in education, indicated by retention rates, which are accumulated as students re-enroll from one academic year to the next. High retention rates can be obtained if institutions aim to provide appropriate support and teaching methods among the various practices to prevent students from deferring their studies. To address this pressing challenge faced by educational institutions, the underlying factors and the methodological aspects of building robust predictive models are reviewed and scrutinized. Educational Data Mining (EDM) and Learning Analytics (LA) have been widely adopted for knowledge discovery from educational data sources, improving the teaching practice, and identifying at-risk students. Various predictive techniques are applied in LA, such as Machine Learning (ML), Statistical Analysis, and Deep Learning (DL). To gain an in-depth review of these techniques, academic publications have been reviewed to highlight their potential to resolve Student Retention issues in education. Additionally, the paper presents a taxonomy of ML approaches and a comprehensive review of the success factors and the features that are not indicative of student performance in three different learning environments: Traditional Learning, Blended Learning, and Online Learning. The survey reveals that supervised ML and DL techniques are broadly applied in Student Retention. However, the application of ensemble and unsupervised learning clustering techniques supporting the heterogenous and homogenous groups of students is generally lacking. Moreover, static and traditional features are commonly used in student performance, ignoring vital factors such as educators-related, cognitive, and personal data. Furthermore, the paper highlights open challenges for future research directions.

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Section: Quality Assessment Resultsmentioning

confidence: 99%

Section: B Dataset Sample Population Size and Collection Methodsmentioning

confidence: 99%

Section: Prediction Approaches Algorithms Learning Outcome Type and T...mentioning

confidence: 99%

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Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

et al. 2022

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“…Educators have experimented with wide variety of input parameters as well as models, for early identification of at‐risk students, in CSE domain. Most of their focus points in this domain are the introductory programming courses [38,39,41,61,62]. Table 1 represents the comparative analysis of some of the relevant research studies in CSE domain.…”

Section: Literature Surveymentioning

confidence: 99%

“…As a result, educators are continuously trying to develop different methodologies for improving the student's ability to learn this computing subject, develop learning progressions and track the students' progress in various simplified ways [21]. The introductory programming‐based courses have attracted the attention of the educators and are often experimented with different pedagogies, due to their large classroom sizes and higher complexities [39,61,62]. However, generalized studies spanning the introductory as well as core subjects of the CSE domain are found to be very limited.…”

Section: Introductionmentioning

confidence: 99%

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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Research on Construction of Student Academic Early Warning Model Based on Ensemble Learning

Li,

2024

Communications in Computer and Information Science

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Using early assessment performance as early warning signs to identify at-risk students in programming courses

Cited by 16 publications

References 33 publications

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

Student Retention Using Educational Data Mining and Predictive Analytics: A Systematic Literature Review

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

Research on Construction of Student Academic Early Warning Model Based on Ensemble Learning

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