Today serious games are having an important impact on areas other than entertainment. Studies show that serious games have a potential of creating learning environments to better reach the educational and training goals. The game design characteristics and game elements are need to be explored in detail for increasing the expected benefits of the gaming environments. In this study, the effect of competition, one of the design elements of game environments, on learning is analyzed experimentally. The study is conducted with 142 students. The results of this study show that when a competition environment is created in a serious game, motivation and post-test scores of learners improve significantly. The results of this study are expected to guide the serious game designers for improving the potential benefits of serious games. © 2015 Elsevier Ltd

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Software engineering is a data-driven discipline and an integral part of data science. The introduction of big data systems has led to a great transformation in the architecture, methodologies, knowledge domains, and skills related to software engineering. Accordingly, education programs are now required to adapt themselves to up-to-date developments by first identifying the competencies concerning big data software engineering to meet the industrial needs and follow the latest trends. This paper aims to reveal the knowledge domains and skill sets required for big data software engineering and develop a taxonomy by mapping these competencies. A semi-automatic methodology is proposed for the semantic analysis of the textual contents of online job advertisements related to big data software engineering. This methodology uses the latent Dirichlet allocation (LDA), a probabilistic topic-modeling technique to discover the hidden semantic structures from a given textual corpus. The output of this paper is a systematic competency map comprising the essential knowledge domains, skills, and tools for big data software engineering. The findings of this paper are expected to help evaluate and improve IT professionals' vocational knowledge and skills, identify professional roles and competencies in personnel recruitment processes of companies, and meet the skill requirements of the industry through software engineering education programs. Additionally, the proposed model can be extended to blogs, social networks, forums, and other online communities to allow automatic identification of emerging trends and generate contextual tags. INDEX TERMS Big data software engineering, competency map, knowledge domains and skill sets, topic modeling, latent Dirichlet allocation. with the Department of Informatics, Karadeniz Technical University, from 2001 to 2014, where he has been an Instructor with the Center for Research and Application in Distance Education, since 2015. His research interests include trend analysis, sentiment analysis, statistical topic modeling, engineering education, data mining, machine learning, big data analytics, and text mining. NERGIZ ERCIL CAGILTAY received the degree in computer engineering and the Ph.D. degree in instructional technologies from Middle East Technical University, Turkey. She worked for commercial and government organizations as a Project Manager for more than eight years in Turkey. She was also with the Indiana University Digital Library Program as a System Analysis and a Programmer for four years. She has been with the Software Engineering Department, Atilim University, Turkey, since 2003, as an Associate Professor. Her main research interests include information systems, medical information systems, engineering education, instructional systems technologies, distance education, e-learning, and medical education.

a b s t r a c tConsidering the role of games for educational purposes, there has an increase in interest among educators in applying strategies used in popular games to create more engaging learning environments. Learning is more fun and appealing in digital educational games and, as a result, it may become more effective. However, few research studies have been conducted to establish principles based on empirical research for designing engaging and entertaining games so as to improve learning. One of the essential characteristics of games that has been unexplored in the literature is the concept of uncertainty. This study examines the effect of uncertainty on learning outcomes. In order to better understand this effect on learning, a game-like learning tool was developed to teach a database concept in higher education programs of software engineering. The tool is designed in two versions: one including uncertainty and the other including no uncertainty. The experimental results of this study reveal that uncertainty enhances learning. Uncertainty is found to be positively associated with motivation. As motivation increases, participants tend to spend more time on answering the questions and to have higher accuracy in these questions.

Software-engineering education programs are intended to prepare students for a field that involves rapidly changing conditions and expectations. Thus, there is always a danger that the skills and the knowledge provided may soon become obsolete. This paper describes results and draws on experiences from the implementation of a computer game-development course whose design addresses problems in software-engineering education by improving students' abilities in four areas: (1) problem solving; (2) the application of previously learned knowledge; (3) the use of independent learning; and (4) learning by doing. In order to better understand this course's effect on students' performance in a software-development project, I investigated 125 students' performance in a 1-year senior-project course. Results of this study show that the students who had taken the computer game-development course became more successful in the senior-project course than the students who had not taken it.

E-learning studies are becoming very important today as they provide alternatives and support to all types of teaching and learning programs. The effect of the COVID-19 pandemic on educational systems has further increased the significance of e-learning. Accordingly, gaining a full understanding of the general topics and trends in e-learning studies is critical for a deeper comprehension of the field. There are many studies that provide such a picture of the e-learning field, but the limitation is that they do not examine the field as a whole. This study aimed to investigate the emerging trends in the e-learning field by implementing a topic modeling analysis based on latent Dirichlet allocation (LDA) on 41,925 peer-reviewed journal articles published between 2000 and 2019. The analysis revealed 16 topics reflecting emerging trends and developments in the e-learning field. Among these, the topics “MOOC,” “learning assessment,” and “e-learning systems” were found to be key topics in the field, with a consistently high volume. In addition, the topics of “learning algorithms,” “learning factors,” and “adaptive learning” were observed to have the highest overall acceleration, with the first two identified as having a higher acceleration in recent years. Going by these results, it is concluded that the next decade of e-learning studies will focus on learning factors and algorithms, which will possibly create a baseline for more individualized and adaptive mobile platforms. In other words, after a certain maturity level is reached by better understanding the learning process through these identified learning factors and algorithms, the next generation of e-learning systems will be built on individualized and adaptive learning environments. These insights could be useful for e-learning communities to improve their research efforts and their applications in the field accordingly.

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