Exploring Self-regulation of More or Less Expert College-Age Video Game Players: A Sequential Explanatory Design

This study examined differences in self-regulation among college-age expert, moderately expert, and non-expert video game players in playing video games for fun. Winnie\u27s model of self-regulation (Winne, 2001) guided the study. The main assumption of this study was that expert video game players used more processes of self-regulation than the less-expert players. We surveyed 143 college students about their game playing frequency, habits, and use of self-regulation. Data analysis indicated that while playing recreational video games, expert gamers self-regulated more than moderately expert and non-expert players and moderately expert players used more processes of self-regulation than non-experts. Semi-structured interviews also were conducted with selected participants at each of the expertise levels. Qualitative follow-up analyses revealed five themes: (1) characteristics of expert video gamers, (2) conditions for playing a video game, (3) figuring out a game, (4) how gamers act and, (5) game context. Overall, findings indicated that playing a video game is a highly self-regulated activity and that becoming an expert video game player mobilizes multiple sets of self-regulation related skills and processes. These findings are seen as promising for educators desiring to encourage student self-regulation, because they indicate the possibility of supporting students via recreational video games by recognizing that their play includes processes of self-regulation

Big five, self-regulation, and coping strategies as predictors of achievement emotions in undergraduate students

The study focused on the analysis of linear relations between personality, self-regulation, coping strategies and achievement emotions. The main objective was to establish a model of linear, empirical, associative to infer needs and proposals for intervening in emotional health in the dierent profiles of university students. A total of 642 undergraduate students participated in this research. Evidence of associative relations between personality factors, self-regulation and coping strategies was found. The neuroticism factor had a significant negative associative relationship with Self-Regulation both globally and in its factors; especially important was its negative relation to decision making, and coping strategies focused in emotion. The results of Structural Equation Model showed an acceptable model of relationships, in each emotional context. Results and practical implications are discussed

Predicting Cognitive Presence in At-Scale Online Learning: MOOC and For-Credit Online Course Environments

In this study, we work towards a strategy to measure and enhance the quality of interactions in discussion forums at scale. We present a machine learning (ML) model which identifies the phase of cognitive presence exhibited by a student’s post and suggest future applications of such a model to help online students develop higher-order thinking. We collect discussion forum transcript data from two online courses: CS1301 (an introductory computer programming MOOC) offered by edX and CS6601 (a graduate course on artificial intelligence) which uses the Piazza online discussion tool. We manually code a random sample of students’ posts based on the Community of Inquiry coding scheme and explore trends in cognitive presence within and across the courses. We further use this coded data to analyze the relationship between students’ observed cognitive presence and course grades. In terms of testing and building an ML model, we use a Bidirectional Encoder Representations from Transformers model that uses a deep learning technique to train large text corpus and fine-tune the language model. Our results suggest that deeper cognitive engagement with course concepts, as expressed by higher cognitive presence, are associated with better learning outcomes for students in both course settings. Our ML approach achieves 92.5% accuracy on the classification task, motivating the use of ML for instructional interventions in online courses. We expect that our research study will not only contribute to extending the literature on cognitive presence but also have a beneficial impact on online instructors or curriculum developers in higher education

Temperature Effects Explain Continental Scale Distribution of Cyanobacterial Toxins

Insight into how environmental change determines the production and distribution of cyanobacterial toxins is necessary for risk assessment. Management guidelines currently focus on hepatotoxins (microcystins). Increasing attention is given to other classes, such as neurotoxins (e.g., anatoxin-a) and cytotoxins (e.g., cylindrospermopsin) due to their potency. Most studies examine the relationship between individual toxin variants and environmental factors, such as nutrients, temperature and light. In summer 2015, we collected samples across Europe to investigate the effect of nutrient and temperature gradients on the variability of toxin production at a continental scale. Direct and indirect effects of temperature were the main drivers of the spatial distribution in the toxins produced by the cyanobacterial community, the toxin concentrations and toxin quota. Generalized linear models showed that a Toxin Diversity Index (TDI) increased with latitude, while it decreased with water stability. Increases in TDI were explained through a significant increase in toxin variants such as MC-YR, anatoxin and cylindrospermopsin, accompanied by a decreasing presence of MC-LR. While global warming continues, the direct and indirect effects of increased lake temperatures will drive changes in the distribution of cyanobacterial toxins in Europe, potentially promoting selection of a few highly toxic species or strains.Peer reviewe

Exploring Self-Regulation of More or Less Expert College-Age Video Game Players: A Sequential Explanatory Design

This study examined differences in self-regulation among college-age expert, moderately expert, and non-expert video game players in playing video games for fun. Winne’s model of self-regulation (Winne, 2001) guided the study. Main assumption of this study was that expert video game players used more processes of self-regulation than the less-expert players. We surveyed 143 college students about their game playing frequency, habits, and use of self-regulation. Data analysis indicated that while playing recreational video games, expert gamers self-regulated more than moderately expert and non-expert players and moderately expert players used more processes of self-regulation than non-experts. Semi-structured interviews also were conducted with selected participants at each of the expertise levels. Qualitative follow-up analyses revealed five themes: 1) characteristics of expert video gamers, 2) conditions for playing a video game, 3) figuring out a game, 4) how gamers act and, 5) game context. Overall, findings indicated that playing a video game is a highly self-regulated activity and that becoming an expert video game player mobilizes multiple sets of self-regulation related skills and processes. These findings are seen as promising for educators desiring to encourage student self-regulation, because they indicate the possibility of supporting students via recreational video games by recognizing that their play includes processes of self-regulation

Exploring self-regulation of more and less expert college-age video game players: A sequential explanatory design

This study explored the self-regulation of recreational video game playing by college age video game players of varying expertise levels. A sequential explanatory mixed methods research design (QUAN→qual) was used. In the quantitative phase of the study, participants were asked to complete the Video Game Playing Survey (VGPS) comprised of three sections: a) General Information, which addressed general video gaming habits and expertise levels; b) How You Play Your Video Games, which used the Playing My Video Game Scale (PMyVGS) to measure self-regulation in video game playing; and c) About Yourself, which collected demographic information of the participant. To classify participants into expertise levels, hierarchical and then k-means cluster analyses were applied to five items in the General Information section of the VGPS. Three expertise levels were detected: expert, moderately expert and non-expert. Since exploratory factor analysis revealed that PMyVGS was a one-dimensional scale, overall PMyVGS scores were analyzed via one way analysis of variance to compare self-regulation of video game players in the expertise groups. Analyses showed that expert video game players used self-regulation processes more than the moderately expert and the non-experts. Likewise the moderately expert players applied the processes more than the non-experts. To follow up quantitative findings, semi-structured interviews were conducted with selected participants at each of the expertise levels. Qualitative analyses revealed five themes which included 1) characteristics of expert video gamers, 2) conditions to play a video game, 3) figuring out a game, 4) how gamers act and,5) game context. First theme reflected participants\u27 descriptions about characteristics of expert video game players. Second theme explained places, times and devices that participants played most often. Third theme reflected how video game players attempted to figure out game rules and context, monitor and control their play and use prior experiences. Fourth theme involved statements about video game players\u27 goals, tactics, the way they seek help, and adapt their play for the next game play. Last theme referred to how a game environment supports game player\u27s monitoring processes, whether video game players shared their gaming experiences with others and whether they use similar strategies for both learning and game playing. Together, the quantitative and qualitative findings indicated that playing a video game is a highly self-regulated activity and that becoming an expert video gamer, in fact, mobilizes multiple sets of skills and processes including self-regulation. These findings are promising for educators who desire to encourage self-regulation, because they may indicate that is possible to support their students via recreational video games by recognizing that their play includes processes of self-regulation

Exploring Self-regulation of More or Less Expert College-Age Video Game Players: A Sequential Explanatory Design

This study examined differences in self-regulation among college-age expert, moderately expert, and non-expert video game players in playing video games for fun. Winnie\u27s model of self-regulation (Winne, 2001) guided the study. The main assumption of this study was that expert video game players used more processes of self-regulation than the less-expert players. We surveyed 143 college students about their game playing frequency, habits, and use of self-regulation. Data analysis indicated that while playing recreational video games, expert gamers self-regulated more than moderately expert and non-expert players and moderately expert players used more processes of self-regulation than non-experts. Semi-structured interviews also were conducted with selected participants at each of the expertise levels. Qualitative follow-up analyses revealed five themes: (1) characteristics of expert video gamers, (2) conditions for playing a video game, (3) figuring out a game, (4) how gamers act and, (5) game context. Overall, findings indicated that playing a video game is a highly self-regulated activity and that becoming an expert video game player mobilizes multiple sets of self-regulation related skills and processes. These findings are seen as promising for educators desiring to encourage student self-regulation, because they indicate the possibility of supporting students via recreational video games by recognizing that their play includes processes of self-regulation

THE EFFECT OF LEARNING STYLES ON ACHIEVEMENT IN DIFFERENT LEARNING ENVIRONMENTS

Every learning environment may attempt to raise successful students, but will not achieve the desired results if several essential elements are not considered in the instructional design process. These elements can be classified interior and exterior conditions. Learner characteristics, items of the interior conditions such as learning style, age, maturity level, interest are essential in designing learning environments process. The purpose of this study is to investigate the effect of learning styles on students ’ achievement in different learning environments which were designed according to principles of Generative Theory of Multimedia Learning. Research was conducted in the framework of single group repeated measures experimental design model and three different learning environments (text based, narration based and computer mediated (narration + music + text + static picture) were planned and study group studied in these environments at different times. The two instruments were used to collect data for this study. The pre-posttest designed to identify students ’ achievement score and Kolb’s Learning Style Inventory to measure students ’ learning style. As a result, it has been clarified that the type of the learning style was not significantly effective on students ’ achievement in different learning environments

A Study of Student's Perceptions in A Blended Learning Environment Based on Different Learning Styles

The rapid growth in the use of learning technologies, particularly the use of the web based technologies and communications have offered educators with many more opportunities to investigate the most suitable learning environments for their students' learning styles. The purpose of the present study was to examine the students' learning styles and their views on blended learning. The study was conducted with thirty-four students at Hacettepe University, Ankara, Turkey. The two instruments were the questionnaire designed to identify students' views on blended learning and Kolb's Learning Style Inventory (LSI) to measure students' learning styles. Additional data were gathered from achievement scores of students; and records demonstrate students' participation to e - learning environment. Results revealed that students' views on blended learning process, such as ease of use of the web environment, evaluation, face to face environment etc., differ according to their learning styles. Results also revealed that the highest mean score corresponds to face to face aspect of the process when students' evaluation concerning the implementation is taken to consideration. The overall findings showed no significant differences between students' achievement level according to their learning styles.Wo

A Study on Students’ Views On Blended Learning Environment

In the 21st century, information and communication technologies (ICT) have developed rapidly and influenced most of the fields and education as well. Then, ICT have offered a favorable environment for the development and use of various methods and tools. With the developments in technology, blended learning has gained considerable popularity in recent years. Together with the developments it brought along the description of particular forms of teaching with technology. Blended learning is defined simply as a learning environment that combines technology with face-to-face learning. In other words blended learning means using a variety of delivery methods to best meet the course objectives by combining face-to-face teaching in a traditional classroom with teaching online. This article examines students’ views on blended learning environment. The study was conducted on 64 students from Department of Computer Education and Instructional Technologies in 2005–2006 fall semester in Instructional Design and Authoring Languages in PC Environment at Hacettepe University. The results showed that the students enjoyed taking part in the blended learning environment. Students’ achievement levels and their frequency of participation to forum affected their views about blended learning environment. Face-to-face interaction in blended learning application had the highest score. This result demonstrated the importance of interaction and communication for the success of on-line learning