Predictors of Errors of Novice Java Programmers

Bringula, Rex P.; Manabat, Geecee Maybelline A.; Tolentino, Miguel Angelo A.; Torres, Edmon L.

doi:10.5430/wje.v2n1p3

Cited by 7 publications

(18 citation statements)

References 39 publications

(56 reference statements)

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“…-Peter J Mellalieu [37] constructed a mining based feature rich prototype Decision Support System (ReXS) with spreadsheet mining to provide his students the provisions to predict their personal academic success and final grade as they progressed through a first-year course 'Innovation and Entrepreneurship'. -R. P. Bringula [38] could able to determine which of the sources of errors would presage the errors committed by tyro Java programmers. Factor analysis showed that there were five categories for the types of errors committed.…”

Section: Student Performance Evaluation Methodsmentioning

confidence: 99%

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A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance

Anoopkumar¹,

Rahman²

2015

Advances in Innovative Engineering and Technologies

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Educational Data Mining (EDM) is an interdisciplinary ingenuous research area that handles the development of methods to explore data arising in a scholastic fields. Computational approaches used by EDM is to examine scholastic data in order to study educational questions. As a result, it provides intrinsic knowledge of teaching and learning process for effective education planning. This paper conducts a comprehensive study on the recent and relevant studies put through in this field to date. The study focuses on methods of analysing educational data to develop models for improving academic performances and improving institutional effectiveness. This paper accumulates and relegates literature, identifies consequential work and mediates it to computing educators and professional bodies. We identify research that gives well-fortified advice to amend edifying and invigorate the more impuissant segment students in the institution. The results of these studies give insight into techniques for ameliorating pedagogical process, presaging student performance, compare the precision of data mining algorithms, and demonstrate the maturity of open source implements.

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Section: Student Performance Evaluation Methodsmentioning

confidence: 99%

“…R. P. Bringula [38] could able to determine which of the sources of errors would presage the errors committed by tyro Java programmers. Delineate statistics brought out that the respondents perceived that they committed the identified eighteen errors infrequently.…”

Section: Mjayakameswaraiah and Sramakrishna [28] Conducted A Study mentioning

confidence: 99%

A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance

Anoopkumar¹,

Rahman²

2015

Advances in Innovative Engineering and Technologies

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“…To date, several studies have been conducted to identify student misconceptions of programming. Notably, novice misconceptions of programming are one of the biggest concerns to programming educators and students (Bringula, Manabat, Tolentino, & Torres, 2012; Kaczmarczyk, Petrick, East, & Herman, 2010; Özdener, 2008).…”

Section: Introductionmentioning

confidence: 99%

Identifying Novice Student Programming Misconceptions and Errors From Summative Assessments

Veerasamy

D'Souza

Laakso

2016

Journal of Educational Technology Systems

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This article presents a study aimed at examining the novice student answers in an introductory programming final e-exam to identify misconceptions and types of errors. Our study used the Delphi concept inventory to identify student misconceptions and skill, rule, and knowledge-based errors approach to identify the types of errors made by novices in Python programming. The students' responses to each question were scrutinized by using the Delphi concept inventory, heuristic-analytic theory, and neo-Piagetian theory of cognitive development for qualitative data analysis. Moreover, the motivation for this exploratory study was to also address the misconceptions that students held in programming and help educators to redefine the teaching methods to correct those alternative conceptions. Student misconceptions were spotted in list referencing and inbuilt functions in Python. In a further quantitative analysis, the study found that students who had misconceptions made knowledge errors and failed to complete the coding tasks. Surprisingly, and coincidentally, it was identified that only a few students were able to write code related to mathematical problems.

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“…Bringula et al [3] studied the sources of programming errors of novice Java programmers (i.e., students). The authors found out that thought error was the main reason why novice programmers committed programming errors.…”

Section: Difficulty In Learning Programmingmentioning

confidence: 99%

“…Programming is one of the core and desirable skills of computing students (e.g., Computer Science, Information Technology, and Information Systems) [3]. In fact, according to JobStreet.com [6], programmers topped the list of highest paid professionals for new graduates in the Philippines.…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Failing the Programming Skill Examination of Computing Students

Bringula¹,

Aviles²,

Batalla³

et al. 2017

IJMECS

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Abstract-This descriptive study utilized a validated instrument to determine the factors that affect failing a programming skill examination. Through this finding, the study attempted to provide solutions to address the concerns of the students. The top three reasons why students failed the programming skill examination were the insufficient time dedicated to programming courses, self-inefficacy in programming, and unmatched questiontime allotment. Overall, respondents attributed their failed mark in programming skill examination to question-related factors. This was confirmed through the use of regression analysis. Hence, it was concluded that students failed the programming skill examination because the perceived ability of the students in the programming skill examination did not correspond with the degree of difficulty of the programming skill examination questions. Further, the null hypothesis stating that student-related concerns do not predict the number of times the programming skill examination would be taken was partially rejected. Hence, it was recommended that the programming skill examination questions be calibrated based on the ability of the students. Future research directions were also presented.

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Predictors of Errors of Novice Java Programmers

Cited by 7 publications

References 39 publications

A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance

A Comprehensive Survey on Educational Data Mining and Use of Data Mining Techniques for Improving Teaching and Predicting Student Performance

Identifying Novice Student Programming Misconceptions and Errors From Summative Assessments

Factors Affecting Failing the Programming Skill Examination of Computing Students

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