A Comparative Study of Engineering Students Pedagogical Progress

Mahboob, Khalid; Syed, Abbas; Ur, Danish; Ali, Fayyaz

doi:10.14569/ijacsa.2018.090646

Cited by 3 publications

(5 citation statements)

References 9 publications

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“…The present study is different from [10][11][12], [15], [17], [19], [29] in a view that many courses consider both sessional and semester final examination marks as well as other assessment parameters like course credit hours, grade point, quality point, and G.P.A with some more classifiers, are used to determine students' academic predictors that may affect their academic results to minimize drop-out ratio to enhance the quality of education in distinguished courses. Further, the academic courses are categorized into technical (considered core) courses, non-technical courses, and mathematical courses to find out how well students have performed and determine their strengths and difficulties in different courses based on their academic results.…”

Section: 𝑅𝑒𝑐𝑎𝑙𝑙 =mentioning

confidence: 97%

“…Social and demographics feature at the school level in [11] are used as influencing factors on students' academic performance. They practice three data mining classification techniques, i.e., Naïve Bayes, J48 decision tree, and Three different classification techniques used in [12], namely Decision Trees, Naïve Bayes, and K-NN of multiple engineering disciplines students taking preexamination marks only with a single course to track and analyze engineering students pedagogical progression in their studies.…”

Section: Related Workmentioning

confidence: 99%

“…There could be more instances in a dataset of better model training. The resultant confusion matrices of technical, non-technical, and mathematical courses comprising both datasets used in this case study are shown in Tables 11,12, and 13, respectively. To understand the results of confusion matrices, let's consider the example of the classifier "k-NN" of a technical course category.…”

Section: 𝑅𝑒𝑐𝑎𝑙𝑙 =mentioning

confidence: 99%

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Quality enhancement at higher education institutions by early identifying students at risk using data mining

Mahboob

Asif

Haider

2023

Mehran Univ. res. j. eng. technol.

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Accurate prediction of students' academic performance is one of the challenges in maintaining quality standards in any Higher Education Institution (H.E.I.). To ensure the quality of teaching and learning, H.E.I.s often employ Self-Assessment Reports (S.A.R.s) in which identifying a student drop-out ratio is important. Hence, it is essential to identify at-risk students in a given academic program. This article aims to identify at-risk students early by proposing a data mining-based predictive framework to improve the student's learning experience and minimize the dropped-out ratio. The academic sub-attributes or indicators in each course that may affect the performance of students in higher education institutions used in this study to examine students' academic achievement and predict students' performance to distinguish at-risk students are the marks of assignments, mid-term, lab exams, semester marks, total, grade, grade point (G.P.), quality point (Q.P.), grade point average (G.P.A.), and credit hours data of multiple courses categorized according to three knowledge areas defined by Higher Education Commission (H.E.C), Pakistan using data mining predictive techniques. The results indicate that the proposed methods can achieve maximum accuracy in predicting and identifying at-risk students in different courses.

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Section: 𝑅𝑒𝑐𝑎𝑙𝑙 =mentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

Section: 𝑅𝑒𝑐𝑎𝑙𝑙 =mentioning

confidence: 99%

See 1 more Smart Citation

Quality enhancement at higher education institutions by early identifying students at risk using data mining

Mahboob

Asif

Haider

2023

Mehran Univ. res. j. eng. technol.

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“…In particular, it can be possible to promptly provide warnings, support low-achieving students, counsel, and provide high-performing students opportunities. Three classification algorithms, including J48 decision trees, k-nearest neighbour, and naïve bayes, have been used in [12] to assess students' performances in different engineering technologies. In this study, only one (primary) course using pre-examination marks of three different engineering technologies belonging to different cohorts are analysed to determine the pedagogical progress of students in their related engineering fields and their learning behaviours in the specific courses to prepare for the final examination based on their pre-examination marks.…”

Section: Related Work On Predicting Graduates' Academic Performancementioning

confidence: 99%

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

Mahboob¹,

Asif²,

Haider³

2023

Mehran Univ. res. j. eng. technol.

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The career opportunities in computer programming are vast and rapidly increasing. Skilled software engineers, programmers, and developers are vigorously in demand worldwide. The capability to forecast a student's future career can be helpful in a wide variety of pedagogical practices. Data mining is becoming a more robust tool for analysis and forecasting. Therefore, to forecast career placement probabilities in the programming field, data mining classification and forecast techniques are used in this study to facilitate prospective students to make sensible career decisions. To achieve this objective, passed-out graduates' data is utilized, which comprises features like graduates' educational attainments in pre-university grades, i.e. grades of matriculation and intermediate, programming courses taught in early semesters along with the Cumulative Grade Point Average (CGPA) with the internship experience, gender, and family demographic information. Various multi-way Classification Trees are generated, which could help students to choose a branch with high career placement probabilities. From historical data, the Classification Trees have determined whether the branch is 'Good', 'Satisfactory', or 'Poor' based on the given information. The experimental findings indicate that all the features significantly influence the career placement probabilities in the programming field.

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“…These analyses have demonstrated their ability to predict student outcomes and identify potential dropouts across a wide range of educational technology contexts. Predictive efforts often center on formal courses like university courses or structured online programs [4,5]. Consequently, the availability of diverse pedagogical data is essential for applying various techniques aimed at enhancing the learning process.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Modeling Approach to Enhance Grade Prediction in Academic Engineering Programming Courses

Mahboob,

Sarfaraz Abdul Sattar Natha,

Syed Saood Zia

et al. 2023

VFAST trans. softw. eng.

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Predicting the future academic grades of students can play a pivotal role in enhancing their performance in specific courses, consequently yielding a positive impact on their prospective academic, professional, and personal achievements, as well as on society at large. The field of programming is rapidly gaining prominence as an essential profession spanning multiple domains, marked by abundant opportunities and financial rewards. To cater to the diverse interests of students, the recommended curriculum structure for engineering programs in computing adeptly combines theoretical knowledge with practical programming skills. This approach ensures that students acquire a comprehensive understanding of programming courses, allowing them to choose the path that aligns best with their envisioned careers as programmers This research endeavors to introduce ensemble prediction techniques aimed at identifying students who exhibit the potential for advancement, or conversely, those who may not excel in four university-level programming courses. The outcomes of this study are presented alongside valuable performance assessment metrics for five ensemble methodologies, namely AdaBoost, Bagging, Random Forest, Stacking, and Voting. This evaluation employs a 10-fold cross-validation methodology and incorporates the Principal Component Analysis (PCA) for feature ranking. The results unequivocally demonstrate that both the Stacking and Random Forest ensemble approaches have attained the highest level of accuracy when applied to two distinct datasets.

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A Comparative Study of Engineering Students Pedagogical Progress

Cited by 3 publications

References 9 publications

Quality enhancement at higher education institutions by early identifying students at risk using data mining

Quality enhancement at higher education institutions by early identifying students at risk using data mining

A data mining approach to forecast students’ career placement probabilities and recommendations in the programming field

An Ensemble Modeling Approach to Enhance Grade Prediction in Academic Engineering Programming Courses

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