Principal Component Clustered Factors for Determining Study Performance in Computer Programming Class

Ninrutsirikun, Unhawa; Imai, Hideyuki; Watanapa, Bunthit; Arpnikanondt, Chonlameth

doi:10.1007/s11277-020-07194-5

Cited by 13 publications

(18 citation statements)

References 38 publications

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“…In accordance with the former psychological literature [10,[12][13][14][15][16][17]19,23,48], an important factor with a high incremental predictive power is EPI, a stable personality trait. On the other hand, the factor that proved to be the most important was a positive psychological factor, namely coping.…”

Section: Discussionsupporting

confidence: 73%

“…The strong effect of personality attributes on university performance has been shown in many studies [12,13]. For example, conscientiousness is found to play a key role in almost every academic field, from programming [14] through psychology [15], to business and management [16]. In addition to conscientiousness, agreeableness and openness also play an important role in academic achievement, according to a meta-analysis by Poropat [12].…”

Section: Introductionmentioning

confidence: 98%

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Traits versus Grades—The Incremental Predictive Power of Positive Psychological Factors over Pre-Enrollment Achievement Measures on Academic Performance

2021

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Understanding what (and to what extent) psychological factors affect university performance has attracted a lot of research interest recently. In this paper, we use logistic regression models to study the incremental predictive power of positive psychological factors over pre-enrollment achievement measures on academic performance. The study is based on the data of 302 business and economics undergraduate students from the Budapest University of Technology and Economics. Coping proved to be the most important factor that sheds light on the importance of stress management for students. We also found that using properly chosen psychological factors measuring coping, personality traits, psychological immune system, emotional intelligence, and PERMA (P—positive emotion, E—engagement, R—relationships, M—meaning, A—accomplishments) factors, together with the university entrance score and academic performance can be predicted significantly better than solely relying on pre-enrollment achievement measures.

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Section: Discussionsupporting

confidence: 73%

Section: Introductionmentioning

confidence: 98%

Traits versus Grades—The Incremental Predictive Power of Positive Psychological Factors over Pre-Enrollment Achievement Measures on Academic Performance

2021

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“…Additionally, the dynamically changing factors that can be identified from programming behaviors, were also scrutinized (Castro-Wunsch, Ahadi & Petersen, 2017). Similarly, the cognitive and non-cognitive factors were analyzed for identifying the students' performance (Ninrutsirikun et al, 2020).…”

Section: Xinogalos 2015mentioning

confidence: 99%

“…McCall & Kölling, 2019;Azcona, Hsiao & Smeaton, 2018;Pereira et al, 2020;Chaweewan et al, 2018;Omer, Farooq & Abid, 2020;Ninrutsirikun et al, 2020;Premchaiswadi, Porouhan & Premchaiswadi, 2018;Turner, Pérez-Quiñones & Edwards, 2018;Ahadi, Hellas & Lister, 2017;Ahmad et al, 2017;al-Rifaie, Yee-King & d'Inverno, 2017;Carter, Hundhausen & Adesope, 2017;Castro-Wunsch, Ahadi & Petersen, 2017;Chung & Hsiao, 2020;Funabiki, Ishihara & Kao, 2016;Watson, Li & Godwin, 2014;Ahadi et al, 2015;Ashenafi, Riccardi & Ronchetti, 2015;Carter, Hundhausen & Adesope, 2015 Approach examination Liao et al, 2019;King, 2018;Lagus et al, 2018;Lin et al, 2018;Santana, Figueredo & Bittencourt, 2018;Effenberger, Pelánek & Čechák, 2020;Echeverría et al, 2017;España-Boquera et al, 2017;Iqbal Malik & Coldwell-Neilson, 2017;Seeling & Eickholt, 2017;Malliarakis, Satratzemi & Xinogalos, 2016;Seeling, 2016;Hilton & Rague, 2015;Janke, Brune & Wagner, 2015;Rubio et al, 2014;Rosiene & Rosiene, 2015;Scott et al, 2015;Wood & Keen, 2015;…”

Section: Features Inspectionmentioning

confidence: 99%

“…Types StudiesModelLiao et al, 2019;Bhatia, Kohli & Singh, 2018;Ninrutsirikun et al, 2020;Effenberger, Pelánek & Čechák, 2020;Iqbal Malik & Coldwell- Neilson, 2017;Ashenafi, Riccardi & Ronchetti, 2015; Carter, Hundhausen & Adesope, 2015 Framework Yeomans, Zschaler & Coate, 2019; Chaweewan et al, 2018; Omer, Farooq & Abid, 2020; Premchaiswadi, Porouhan & Premchaiswadi, 2018 Experiment McCall & Kölling, 2019; Ullah et al, 2019; Ureel & Wallace, 2019; Pereira et al, 2020; Esteero et al, 2018; Gomes & Correia, 2018;King, 2018;Lin et al, 2018;Turner, Pérez-Quiñones & Edwards, 2018;Ahadi, Hellas & Lister, 2017;Ahmad et al, 2017;Delev & Gjorgjevikj, 2017;Dorodchi, Dehbozorgi & Frevert, 2017;Echeverría et al, 2017;España-Boquera et al, 2017;Estey, Keuning & Coady, 2017;Chung & Hsiao, 2020;Fu et al, 2017;Hsiao, Huang & Murphy, 2017;Kumar, 2017;Seanosky et al, 2017;Berges et al…”

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confidence: 99%

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Introductory programming course: review and future implications

Omer

Farooq

Abid

2021

PeerJ Computer Science

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The introductory programming course (IPC) holds a special significance in computing disciplines as this course serves as a prerequisite for studying the higher level courses. Students generally face difficulties during their initial stages of learning how to program. Continuous efforts are being made to examine this course for identifying potential improvements. This article presents the review of the state-of-the-art research exploring various components of IPC by examining sixty-six articles published between 2014 and 2020 in well-reputed research venues. The results reveal that several useful methods have been proposed to support teaching and learning in IPC. Moreover, the research in IPC presented useful ways to conduct assessments, and also demonstrated different techniques to examine improvements in the IPC contents. In addition, a variety of tools are evaluated to support the related course processes. Apart from the aforementioned facets, this research explores other interesting dimensions of IPC, such as collaborative learning, cognitive assessments, and performance predictions. In addition to reviewing the recent advancements in IPC, this study proposes a new taxonomy of IPC research dimensions. Furthermore, based on the successful practices that are listed in the literature, some useful guidelines and advices for instructors have also been reported in this article. Lastly, this review presents some pertinent open research issues to highlight the future dimensions for IPC researchers.

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