A Meta-analysis of Educational Data Mining for Predicting Students Performance in Programming

In this competitive scenario of the educational system, higher education institutions use intelligent learning tools and techniques to predict the factors of student academic performance. Given this, the article aims to determine the supervised learning model for the predictive system of personal and social attitudes of university students of professional engineering careers. For this, the Machine Learning Classification Learner technique is used by means of the Matlab R2021a software. The results reflect a predictive system capable of classifying the four satisfaction classes (1: dissatisfied, 2: not very satisfied, 3: satisfied and 4: very satisfied) with an accuracy of 91.96%, a precision of 79.09%, a Sensitivity of 75.66% and a Specificity of 92.09%, regarding the students' perception of their personal and social attitudes. As a result, the higher institution will be able to take measures to monitor and correct the strengths and weaknesses of each variable related to satisfaction with the quality of the educational service.

Section: Discussionmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students

Chamorro-Atalaya¹,

Olivares-Zegarra²,

Paredes-Soria³

et al. 2021

“…As indicated in [6], researchers have been concerned in recent years to work on the development of models that allow understanding aspects of the academic life of the student, teachers and institutions that allow the preparation and making of correct decisions, for the improvement continuity of educational quality. Likewise, in [19] it is indicated that the results obtained and validations show a precision of 82%, therefore, it can be pointed out that the process describes an optimal performance of the algorithms, so its incorporation would be satisfactory to be incorporated to the management of virtual educational knowledge.…”

Section: Discussionmentioning

confidence: 99%

“…The advantages that the introduction of ICT has generated in the education sector is based on the importance of technology to develop research that previously could not be carried out, [6], [7] as is the case of the identification of predictive models for the analysis or monitoring of university teaching performance, student performance, among other relevant factors for the education sector [8]- [10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance

Chamorro-Atalaya¹,

Chávez-Herrera²,

Santos³

et al. 2021

The objective of this study is to analyze and discuss the metrics of the Machine Learning model through the Ensemble Bagged Trees algorithm, which will be applied to data on satisfaction with teaching performance in the virtual environment. Initially the classification analysis through the Matlab R2021a software, identified an Accuracy of 81.3%, for the Ensemble Bagged Trees algorithm. When performing the validation of the collected data, and proceeding with the obtaining of the predictive model, for the 4 classes (satisfaction levels), total precision values of 82.21%, Sensitivity of 73.40%, Specificity of 91.02% and of 90.63% Accuracy. In turn, the highest level of the area under the curve (AUC) by means of the Receiver operating characteristic (ROC) is 0.93, thus considering a sensitivity of the predictive model of 93%. The validation of these results will allow the directors of the higher institution to have a database, to be used in the process of improving the quality of the educational service in relation to teaching performance.

“…Intelligent Tutoring System (ITS) is the recently proposed computer-based teaching context to help students learn programing languages but to the best of our knowledge, computer-based applications for teaching programming are not widely implemented [2]. With rising novice computer scientists, specific questions are generated which can address knowledge gaps that were often negligible in the manual process of articulating questions [6]. In support of enhancing the metacognitive skills of students, asking students to generate questions can be a constructive process but the use of various metacognitive skills can be time-consuming and needs extended knowledge of various metacognitive strategies.…”

Section: A Backgroundmentioning

confidence: 99%

A Review of Automatic Question Generation in Teaching Programming

Alshboul¹,

Baksa-Varga²

2022

Computer programming is a complex field that requires rigorous practice in programming code writing and learning skills, which can be one of the critical challenges in learning and teaching programming. The complicated nature of computer programming requires an instructor to manage its learning resources and diligently generate programming-related questions for students that need conceptual programming and procedural programming rules. In this regard, automatic question generation techniques help teachers carefully align their learning objectives with the question designs in terms of relevancy and complexity. This also helps in reducing the costs linked with the manual generation of questions and fulfills the need of supplying new questions through automatic question techniques. This paper presents a theoretical review of automatic question generation (AQG) techniques, particularly related to computer programming languages from the year 2017 till 2022. A total of 18 papers are included in this study. one of the goals is to analyze and compare the state of the field in question generation before COVID-19 and after the COVID-19 period, and to summarize the challenges and future directions in the field. In congruence to previous studies, there is little focus given in the existing literature on generating questions related to learning programming languages through different techniques. Our findings show that there is a need to further enhance experimental studies in implementing automatic question generation especially in the field of programming. Also, there is a need to implement an authoring tool that can demonstrate designing more practical evaluation metrics for students.