A Solution to Manage the Full Life Cycle of Learning Analytics in a Learning Management System: AnalyTIC

Herrera, Fredys Alberto Simanca; Crespo, Rubén González; Baena, Luis Rodríguez; Burgos, Daniel

doi:10.1109/rita.2019.2950148

Cited by 6 publications

(11 citation statements)

References 15 publications

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“…Learning Engagement [22], [34], [39], [40], [42], [43], [46], [49], [52], [53], [58], [67]- [70], [76], [87] In accordance with the findings presented in [23], academic performance pertains to the achievement of students in their pursuit of higher education, typically assessed through their academic grades or performance on examinations and assessments [18], [23], [35], [45], [64], [80], with particular emphasis on their initial year of study. Additionally, it encompasses the probability of students' persistence in pursuing further education or their decision to discontinue their academic pursuits.…”

Section: Table 4 Mapping Of Approachesmentioning

confidence: 65%

“…No Approach Paper 1 Academic Performance Prediction [11], [12], [17], [18], [23], [24], [35], [37], [38], [41], [44], [45], [51], [56], [57], [64], [71], [74], [75], [78], [80], [81], [84] 2 At-Risk Student Prediction [1], [9], [10], [13], [19], [36], [50], [55], [59], [62], [65], [66], [73], [77], [79], [82] 3 Dropout/Graduation Prediction [14], [15], [20], [21], [47], [48], [54], [60], [61], [63], [72],…”

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

“…The prompt identification of students' academic performance is widely recognized as a vital asset for enhancing the quality of education [71]. Researchers are showing interest in predicting academic performance, this can be seen from increasing numbers of research on predicting academic performance as well as selected primary publications stating that it is beneficial to students [23], [24], [35], [44], [45], [51], [71], [74], [78], [80], [84], instructors [11], [12], [17], [18], [23], [24], [37], [38], [41], [44], [45], [57], [64], [75], [78], educational institutions [17], [23], [24], [37], [78]. From the discussion above, it can be concluded that there are several objectives behind predicting academic performance:…”

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

“…The utilization of predictive insights has the potential to enhance the creation of customized or adaptable learning experiences that suit the unique needs of individual students. For instance, students who are anticipated to encounter difficulties in a specific academic discipline should be offered further materials or alternate pedagogical approaches [11], [18], [24], [41], [45], [51]. • Data-driven Decision Making.…”

Section: • Resourcementioning

confidence: 99%

See 3 more Smart Citations

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Pelima,

Sukmana,

Rosmansyah

2024

IEEE Access

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Predicting university student graduation is a beneficial tool for both students and institutions. With the help of this predictive capacity, students may make well-informed decisions about their academic and career paths, and institutions can proactively identify students who may not graduate and offer tailored support to ensure their success. The use of machine learning for predicting university student graduation has drawn more attention in recent years. Large datasets of student academic performance data can be used to train machine learning algorithms to identify patterns that are applicable in predicting future outcomes. In accordance with some studies, this approach predicts student graduation with an accuracy rate as high as 90%. Many SLRs have been conducted in this field, but there are still limitations, including not discussing the predictive models and algorithms used, a lack of coverage of the machine learning algorithms applied, small database coverage, keyword selection that does not cover all synonyms relevant to the investigation, and less specific data collection transparency. By delving into the limitations of existing SLRs on this topic, this research not only enhances the understanding of machine learning applications in forecasting student graduation but also fills a crucial gap in the literature. The inclusion of weaknesses in current SLRs provides a foundation for justifying the need for this study, emphasizing the necessity of a more nuanced and comprehensive review to advance the field and guide future research efforts in smart learning environments. This research conducts a thorough systematic review of the existing literature on machine learning-based student graduation prediction models from 70 journal articles from 2018 through 2023 that are pertinent. This review includes the various machine learning algorithms that have been implemented, the various academic performance data that was obtained from students, and the effectiveness of the models that have been developed. It also discusses the difficulties and potential advantages of utilizing machine learning to predict student graduation. The review indicates that the most common approach employed is the prediction of students' academic performance, which relies on data obtained from the Learning Management System and Student Information System. The primary data utilized for prediction purposes consists Student retention and time of academic and behavioral information. Among the various algorithms employed, Support Vector Machine and Random Forest are the most commonly utilized. This study makes a significant contribution to the advancement of learner modules within the smart learning environment.

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Section: Table 4 Mapping Of Approachesmentioning

confidence: 65%

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

Section: Table 4 Mapping Of Approachesmentioning

confidence: 99%

Section: • Resourcementioning

confidence: 99%

See 2 more Smart Citations

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Pelima,

Sukmana,

Rosmansyah

2024

IEEE Access

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show abstract

“…Regression testing merupakan salah satu aktivitas pengujian software yang esensial digunakan pada metode pengembangan software dengan siklus perubahan secara berskala pada agile development (Faizullah and Almutairi, 2018). Regression testing digunakan untuk menemukan defect atau error pada bagian software yang sudah dipastikan benar sebelumnya sebagai akibat dari software yang telah dimodifikasi (Simanca et al, 2019) (Rosero, Gómez and Rodríguez, 2017) (Ali et al, 2019). Proses pelaksanaan regression testing, yaitu menjalankan kembali test case yang telah dijalankan untuk memeriksa defect baru atau defect yang telah diperbaiki muncul kembali (Rosero, Gómez and Rodríguez, 2017).…”

Section: Pendahuluanunclassified

Perbandingan Kemampuan Regression Testing Tool Pada Regression Test Selection: Starts Dan Ekstazi

Maspupah¹,

Bakhrun²

2021

JTT

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Regression testing as an essential activity in software development that has changed requirements. In practice, regression testing requires a lot of time so that an optimal strategy is needed. One approach that can be used to speed up execution time is the Regression Test Selection (RTS) approach. Currently, practitioners and academics have started to think about developing tools to optimize the process of implementing regression testing. Among them, STARTS and Ekstazi are the most popular regression testing tools among academics in running test case selection algorithms. This article discusses the comparison of the capabilities of the STARTS and Ekstazi features by using feature parameter evaluation. Both tools were tested with the same input data in the form of System Under Test (SUT) and test cases. The parameters used in the tool comparisons are platform technology, test case selection, functionality, usability and performance efficiency, the advantages, and disadvantages of the tool. he results of the trial show the differences and similarities between the features of STARTS and Ekstazi, so that it can be used by practitioners to take advantage of tools in the implementation of regression testing that suit their needs. In addition, experimental results show that the use of Ekstazi is more precise in sorting out important test cases and is more efficient, when compared to STARTS and regression testing with retest all.

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Model for the collection and analysis of data from teachers and students supported by Academic Analytics

Simanca

Arteaga

Puin

et al. 2020

Procedia Computer Science

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A Solution to Manage the Full Life Cycle of Learning Analytics in a Learning Management System: AnalyTIC

Cited by 6 publications

References 15 publications

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Predicting University Student Graduation Using Academic Performance and Machine Learning: A Systematic Literature Review

Perbandingan Kemampuan Regression Testing Tool Pada Regression Test Selection: Starts Dan Ekstazi

Model for the collection and analysis of data from teachers and students supported by Academic Analytics

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