Work in progress: Analysis of the relationship between teaching contents and motivation in programming education

Tsukamoto, Hidekuni; Takemura, Yasuhiro; Nagumo, Hideo; Nitta, Naoya

doi:10.1109/fie.2012.6462408

Cited by 7 publications

(3 citation statements)

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“…Gamification can facilitate motivation which brings about the engagement of users, resulting in higher overall performance and alternate of behavior [3]. Consequently, [4][5][6][7] elucidated that an strategy which can effortlessly amplify the engagements and applied in an exciting manner (such as games like elements) ought to be adopted in gaining knowledge of difficult subjects. Gamification refers to the use of recreation factors in a non-game context to expand engagement between human beings and computer systems and to clear up troubles correctly [8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The impact of gamification on students learning engagement

Khaleel

Sahari

Wook

2020

IJECE

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Gamification is to use game elements in a non-game context to increase engagement between human and computer, on the other hand, to encourage in-demand for good behaviors in learning. This research tried to increase student engagements in learning by conducted Gamification technique especially in difficult subjects such as Programming Language courses. The previous work was shown that students dropped, failed, or withdrew from the course at rates of between 35% and 50%. Therefore the main objective of this study is to increase student engagements in learning programming subject, and also to measure the impact of game elements on student’s engagements. Finally, the findings have shown the score of game elements that have a good effect on student’s engagement in the experiment group.

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Section: Introductionmentioning

confidence: 99%

The impact of gamification on students learning engagement

Khaleel

Sahari

Wook

2020

IJECE

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“…Students' strategic self-regulation has been identified as playing a critical role in their success in STEM learning [3,4], but how students' use of strategy, metacognitive selfregulation, and engagement impact different types of learning outcomes is not fully understood. Also, despite considerable past research, including recent work reported in prior Frontiers in Education conference proceedings [5,6,7], theory and research have not completely described the dynamics of student motivation for pursuing productive strategic self-regulation and engagement. Our goal in this study was to investigate how motivation and strategic self-regulation, together with creative competency, were associated with course achievement and long-term learning of computational thinking knowledge and skills in introductory computer science (CS1) courses.…”

Section: Introductionmentioning

confidence: 99%

Associations of students' creativity, motivation, and self-regulation with learning and achievement in college computer science courses

Shell

Hazley

Soh

et al. 2013

2013 IEEE Frontiers in Education Conference (FIE)

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The need for more post-secondary students to major and graduate in STEM fields is widely recognized. Students' motivation and strategic self-regulation have been identified as playing crucial roles in their success in STEM classes. But, how students' strategy use, self-regulation, knowledge building, and engagement impact different learning outcomes is not well understood. Our goal in this study was to investigate how motivation, strategic self-regulation, and creative competency were associated with course achievement and long-term learning of computational thinking knowledge and skills in introductory computer science courses. Student grades and long-term retention were positively associated with self-regulated strategy use and knowledge building, and negatively associated with lack of regulation. Grades were associated with higher study effort and knowledge retention was associated with higher study time. For motivation, higher learning-and task-approach goal orientations, endogenous instrumentality, and positive affect and lower learning-, task-, and performance-avoid goal orientations, exogenous instrumentality and negative affect were associated with higher grades and knowledge retention and also with strategic selfregulation and engagement. Implicit intelligence beliefs were associated with strategic self-regulation, but not grades or knowledge retention. Creative competency was associated with knowledge retention, but not grades, and with higher strategic self-regulation. Implications for STEM education are discussed.

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“…In the past, the authors had been analyzing motivation of the learners in programming education using the ARCS assessment metric [3][4][5][6][7]. In this metric, motivation is assessed using four factors, namely, Attention, Relevance, Confidence, and Satisfaction separately.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the change of learners' motivation in programming education for non-computing majors

Tsukamoto

Takemura

Nagumo

et al. 2014

2014 IEEE Frontiers in Education Conference (FIE) Proceedings

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In the past, the authors had been analyzing motivation of the learners in programming education using the ARCS assessment metric. This metric had been used in the application experiment in 13 programming courses, and about 1,700 sets of data was collected. From these data, the learners' model, characteristics of the change of motivation, and ways of improving teaching materials had been clarified. However, these study results were obtained after the terms, when the programming courses were over, and thus did not contribute much to the ongoing programming education. For this reason, in this research, the methods for predicting the change of learners' motivation were studied so that the learners who may need support could be identified. The idea came from the experiment the authors conducted, in which the motivation of learners was analyzed by plotting the motivation scores of each factor in the ARCS model as a 3D graph. As a result, a decreasing tendency of motivation was observed when the distribution of the plot widened. After studying the tendency in detail, it was thought to be due to the influence of the variance of sub-level category scores. In the proposed method, the motivation of each learner is assessed in each lesson using the ARCS assessment metric. If variance of the motivation scores of a learner in a lesson is above a certain threshold value AND if mean of the scores has not decreased from the previous lesson, then the learner is identified as a candidate of learner who needs support at that lesson. In the application experiment, a programming course with 9 lessons was offered and 9 learners attended all the 9 lessons. In the experiment, 7 cases had been identified as the candidates of learners who need support, and out of those 7 cases, a decrease of motivation to less than average was observed in 5 cases.

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Work in progress: Analysis of the relationship between teaching contents and motivation in programming education

Cited by 7 publications

References 10 publications

The impact of gamification on students learning engagement

The impact of gamification on students learning engagement

Associations of students' creativity, motivation, and self-regulation with learning and achievement in college computer science courses

Prediction of the change of learners' motivation in programming education for non-computing majors

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