Gamification in Computer Programming Education – Systematic Literature Review

Kučak, Danijel; Kucak, Martina

doi:10.23919/mipro55190.2022.9803457

Cited by 10 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the introduction of both duels and virtual currency resulted in a rise in student intrinsic motivation. Teaching principles and paradigms of computer programming to train future software engineers can be a difficult task [18]. The concepts involved can often be abstract, posing challenges for students.…”

Section: Related Workmentioning

confidence: 99%

Applying a New Game Element Called Backward Grading For Student Engagement

Çulha

2024

rev.digit.educ.ing

View full text Add to dashboard Cite

Student engagement refers to how actively involved and enthusiastic a student is about their learning process. Engaged students are actively involved in their education, show a willingness to learn, and are motivated to achieve their academic goals. Student engagement is important for learning activities and can be achieved via gamification. In this work, a gamification approach was designed that incorporates game elements such as tournament, teams, multidisciplinary challenges, grading, backward grading, badges, points, and leaderboard. Especially the new game element backward grading is designed to achieve student engagement. The game element grading promotes competitions among student teams. On the other hand, the novel game element backward grading promotes contributions to competitions. The designed gamification setting is applied to an emerging technologies course, and the results show that student engagement is achieved.

show abstract

Section: Related Workmentioning

confidence: 99%

Applying a New Game Element Called Backward Grading For Student Engagement

Çulha

2024

rev.digit.educ.ing

View full text Add to dashboard Cite

show abstract

“…Today's children grew up in an environment where they had fun with computer games, and as a result, gamification has entered the education system (Kučak & Kučak, 2022;Swacha, 2021). It is seen that today's students, most of whom are Generation Z, are searching for innovative and alternative learning methods (Aktaş & Baykara, 2023).…”

Section: Emotions Are Critical To Patterningmentioning

confidence: 99%

An Investigation of Brain-Based Learning Principles in Today's Changing Conditions

Korkmaz

2024

Anadolu Üniversitesi Eğitim Fakültesi Dergisi

View full text Add to dashboard Cite

The increasing prevalence of brain research and the fact that the findings reveal important arguments about the learning process have led to the proliferation of brain-based learning studies. Brain-based learning principles, introduced by Caine and Caine in 1991, are frequently used in the literature and shed light on educational research. However, in today's changing conditions, educational environments have remarkably differentiated, the opportunities for learners have increased more than ever before, threats such as the Covid-19 pandemic and natural disasters have directly affected education, and technological advances have made a breakthrough with artificial intelligence. There are studies evaluating 12 accepted and frequently used brain-based learning principles in the literature, but there is no review study that addresses brain-based learning principles in today's changing conditions. Therefore, this article aims to examine the principles of brain-based learning in today's changing conditions. Increasing threats in the rapidly changing world pose a risk to brain-based learning. Also, increasingly common helicopter parenting and artificial intelligence hinder brain-based learning, although they have some advantages. However, on the other hand, the fact that technological developments make educational environments richer and increase the opportunities for learners greatly facilitates brain-based learning. Thus, brain-based learning principles have become much more meaningful and gained great importance in today's world.

show abstract

“…One of AdaGrad's most important modifications is the RMSProp algorithm, solving the previous weaknesses found on it [20][21][22][23][24][25]. There are also other learning methods for the NN that give more precise learning ratios, such as the Levenberg-Marquard algorithm [26][27][28][29][30][31][32][33]. This method gives good results on the system loss but requires a high consumption of time and computational resources that could result in the training process not easily handling even the simplest problems.…”

Section: Introductionmentioning

confidence: 99%

Modification of Learning Ratio and Drop-Out for Stochastic Gradient Descendant Algorithm

et al. 2023

View full text Add to dashboard Cite

The stochastic gradient descendant algorithm is one of the most popular neural network training algorithms. Many authors have contributed to modifying or adapting its shape and parametrizations in order to improve its performance. In this paper, the authors propose two modifications on this algorithm that can result in a better performance without increasing significantly the computational and time resources needed. The first one is a dynamic learning ratio depending on the network layer where it is applied, and the second one is a dynamic drop-out that decreases through the epochs of training. These techniques have been tested against different benchmark function to see their effect on the learning process. The obtained results show that the application of these techniques improves the performance of the learning of the neural network, especially when they are used together.

show abstract

Gamification in Computer Programming Education – Systematic Literature Review

Cited by 10 publications

References 9 publications

Applying a New Game Element Called Backward Grading For Student Engagement

Applying a New Game Element Called Backward Grading For Student Engagement

An Investigation of Brain-Based Learning Principles in Today's Changing Conditions

Modification of Learning Ratio and Drop-Out for Stochastic Gradient Descendant Algorithm

Contact Info

Product

Resources

About