A Semi-Supervised Learning Approach for Predicting Student's Performance: First-Year Students Case Study

Widyaningsih, Yekti; Fitriani, Nur; Sarwinda, Devvi

doi:10.1109/icts.2019.8850950

Cited by 11 publications

(16 citation statements)

References 3 publications

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“…A semi-supervised learning approach is the one they used [14] to rank the performance of first-year college students. The categories to classify were low, medium and high and the classifier was Naive Bayes, who obtained an accuracy of 96% and specificity of 100%.…”

Section: Related Workmentioning

confidence: 99%

“…Ada Boost classifiers represent a robust class of classifiers that aim to increase or improve the accuracy of an already built classifier [14].…”

Section: Ada Boostmentioning

confidence: 99%

“…For the development of the present study, the steps of the stacking method shown in Fig. 2 have been followed, it should be noted that XGBoost is an advanced software based on Gradient Tree Boosting that can efficiently handle large-scale machine learning tasks [14].…”

Section: F Stackingmentioning

confidence: 99%

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Predicting Academic Performance using a Multiclassification Model: Case Study

Vergaray¹,

Guerra²,

Cervera³

et al. 2022

IJACSA

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Now-a-days predicting the academic performance of students is increasingly possible, thanks to the constant use of computer systems that store a large amount of student information. Machine learning uses this information to achieve big goals, such as predicting whether or not a student will pass a course. The main purpose of the work was to make a multiclassifier model that exceeds the results obtained from the machine learning models used independently. For the development of our proposed predictive model, the methodology was used, which consists of several phases. For the first step, 557 records with 25 characteristics related to academic performance were selected, then the preprocessing was applied to said data set, eliminating the attributes with the lowest correlation and those records with inconsistencies, leaving 500 records and 9 attributes. For the transformation, it was necessary to convert categorical to numerical data of four attributes, being the following: SEX, ESTATUS_lab_padre, ESTATUS_lab_madre and CONDITION. Having the data set clean, we proceeded to balance the data, where 1,167 data were generated, using the 2/3 for training and the remaining 1/3 for validation, then the following techniques were applied: Extra Tree, Random Forest, Decision Tree, Ada Boost and XGBoost, each obtained an accuracy of 57.41%, 61.96%, 91.44%, 59.65% and 83.3% respectively. Then the proposed model was applied, combining the five algorithms mentioned above, which reached an accuracy of 92.86%, concluding that the proposed model provides better accuracy than when the models are used independently meaning that it was the one that obtained the best result.

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Section: Related Workmentioning

confidence: 99%

“…Ada Boost classifiers represent a robust class of classifiers that aim to increase or improve the accuracy of an already built classifier [14].…”

Section: Ada Boostmentioning

confidence: 99%

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Predicting Academic Performance using a Multiclassification Model: Case Study

Vergaray¹,

Guerra²,

Cervera³

et al. 2022

IJACSA

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“…39 Researchers who collect data manually through the questionnaire usually design the questions in the questionnaire according to the predicted specific objectives. [40][41][42][43][44][45][46][47] Especially, Hoang et al conducted an online survey with a participation of students in various courses to analyze and show the effects of different learning styles on students' performance. 43 That test is based on the Felder-Soloman questionnaire 48 with 44 questions which are divided into four dimensions.…”

Section: Source Of Datamentioning

confidence: 99%

A survey on educational data mining methods used for predicting students' performance

Xiao

2021

Engineering Reports

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Predicting students' performance is one of the most important issues in educational data mining (EDM), which has received more and more attention. By predicting students' performance, we can identify students' risk of academic failure and help instructors to take some actions such as guidance or interventions to help learners as early as possible, or carry out continual evaluation of learners as to optimize learning path or personalized learning resources recommendation. In this survey, we reviewed the 80 important studies on predicting students' performance using EDM methods in 2016-2021, synthesized the procedure of building prediction model of students' performance which contains four phases and 10 key steps, and compared and discussed the latest EDM methods used in all steps. We analyzed the challenges faced by previous studies in three aspects and put forward future suggestions on data collection, EDM methods used, and interpretation of prediction model. This survey provides a comprehensive understanding and practical guide for researchers in this field, and also provides direction for further research.

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“…Tang et al: (2015), Klusener and Fortenbacher (2015), Brinton and Chiang (2015), Shrivas and T iwari b. Articles: Al Shehri et al: (2017), Amaya et al: (2015), Widyaningsih et al: (2019) c. Articles: Okubo et al: (2017), Singh and Kaur (2018), Santoso and Yulia (2019), Sumitha et al: (2016), Al Barrak and Al Razgan d. Articles: Amrieh et al: (2015), Ruby and David (2015), Almasri et al: (2019), Sivakumar et al: (2016), Kumar et al: (2019), Chanlekha and Niramitranon (2018) e. Articles: Livieris et al: (2018), Angiani et al: (2019), Jishan et al: (2015), Livieris et al: (2019c), Al Saleem et al: (2015, f. Articles: Kostopoulos et al: (2015), Athani et al: (2017), Sara et al: (2015), Kasthuriarachchi and Liyanage (2019), Namomsa andSharma (2018) g. Articles: Bergin et al: (2015), Navamani and Kannammal (2015), Ketui et al (2019), Bhegade and Shinde (2016), Pristyanto et al, (2018) h. Articles: Lopez Guarin et al: (2015, Pradeep et al: (2015), Yehuala (2015), Kaur and Singh (2016), Mahboob et al: (2017), Pereira et al, (2018)…”

Section: Attributes Usedmentioning

confidence: 99%

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

Papadogiannis

Poulopoulos

Wallace

2020

International Journal of Educational Research Review

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This article provides a thorough review of educational data mining (EDM) in the period 2015-2019. Going beyond earlier review works, in this article we examine previous research from a variety of aspects, including the examined data, the algorithms used, the type of conclusions drawn, the educational level/setting of application and the actual exploitation of the results in the educational setting. Our findings indicate that tertiary education dominates the EDM domain, while minimal focus has been given to secondary education and almost none to primary education. Our finding, and suggestion, is that by focusing EDM on earlier education level the field can have a more profound impact on education and on society as a whole.

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A Semi-Supervised Learning Approach for Predicting Student's Performance: First-Year Students Case Study

Cited by 11 publications

References 3 publications

Predicting Academic Performance using a Multiclassification Model: Case Study

Predicting Academic Performance using a Multiclassification Model: Case Study

A survey on educational data mining methods used for predicting students' performance

A Critical Review of Data Mining for Education: What has been done, what has been learnt and what remains to be seen

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