Usability is critical to the development of a user-friendly digital textbook platform interface, yet thorough research on interface development based on usability principles is in short supply. This study addresses that need by looking at usability attributes and corresponding design elements from a learning perspective. The researchers used a student survey, log files, and an expert heuristic evaluation to analyze needs for revision in the user interface of the existing Korea Education Research Information Service digital textbook platform. After using suggestions derived from this analysis to develop a new platform prototype, they tested its user interface for usability through a cognitive walkthrough and a formative evaluation. The results show that the usability design elements identified through the use of this iterative design and evaluation model were essential to improving the usability of the user interface and thus facilitating users' actions and learning processes.
<p>Despite continuous efforts to increase retention, dropout rates are high in distance universities. The objectives of this study were: 1) to investigate the extent and causes of non re-enrollment at a mega university, Korea National Open University; and 2) to suggest actions to improve the retention of students, in general, and those with higher risks of dropout in particular. A survey designed to establish the student demographics and the students’ main reasons for non re-enrollment was carried out during spring, 2009 with 1,353 respondents. The results indicate that a lack of feedback from the instructors, heavy workload, and difficulties in studying at a distance were the main reasons for non re-enrollment. The learners’ perceptions of the value of the degrees and their ages, gender, and educational backgrounds were also found to be significant factors in decisions not to re-enroll. The suggested solutions for reducing non re-enrollment include: a decrease in the number of required credit hours’ study per semester; the provision of stronger social support; the introduction of a more flexible enrollment system; and better use of the available technology and infrastructure to help both students and instructors build stronger learning communities.</p>
Current recommendations for achieving high-quality professional development for teachers include the creation of a professional learning community. Key to the success of this approach, however, is the role of the peer mentor or coach. This study examined the experiences and perceptions of 31 professional development coaches in order to highlight the characteristics believed to be essential for success. Results suggest that, while content expertise is perceived to be important, coaches believe that strong interpersonal skills are more critical since, without them, they are unable to use their content knowledge to facilitate changes in teachers’ practice. Suggestions for selecting and training peer coaches are included.
The main purpose of this study was to demonstrate the uses of regularization, a machine learning technique, in exploring important predictors for online student success. We analyzed student and learning behavioral variables from undergraduate fully-online flipped classrooms. In particular, students' instructional video watching behaviors at an instructional unit level were extracted from LMS (learning management system) log data, and Enet (elastic net) and Mnet were employed among regularization. As results, regularization not only showed comparable prediction performance to random forest, a nonlinear method well-known for its prediction capabilities, but also produced interpretable prediction models as a linear method. Enet and Mnet selected 17 and 19 important predictors out of 159, respectively, and could identify potential low-performers as early as the first instructional week of the course. Important variables rarely recognized in previous studies included complete viewings of the first video before class and repeated complete viewings of challenging contents after in-class meetings. Unlike previous studies, aggregate measures of video lecture views were not important predictors. Variables less frequently studies in previous studies were the number of non-mandatory quiz-taking and mobile lecture watching frequencies. Variables in line with previous research were student attitudes towards the course, gender, grade level, and clicks on learning materials postings. Many students turned out not to watch lecture videos completely before class. Further research on regularization and exploration of these variables with other potentially important predictors can provide more insight into students' online learning from a comprehensive perspective.
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