Typical data science instruction uses generic datasets like survival rates on the Titanic, which may not be motivating for students. Will introducing real-life data science problems fill this motivational deficit? To analyze this question, we contrasted learning with generic datasets and artificial problems (Phase 1) with a community-sourced…

This study examined the predictive power of students' demographic characteristics, reading attitudes, school characteristics, and teacher-informed reading activities on three metacognitive reading skills: understanding and remembering, summarizing, and assessing credibility and their influence on 15-year-old students' reading scores. The dataset…

Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and…

Within the last decade, different educational data mining techniques, particularly quantitative methods such as clustering, and regression analysis are widely used to analyze the data from educational games. In this research, we implemented a quantitative data mining technique (clustering) to further investigate students' feedback. Students played…

This paper presents the results of an attempt to analyze the benefits and risks of information and communication technologies (ICTs) and their applications, as perceived by university students. A survey was conducted using questionnaire with a free descriptive answering format. Responses were analyzed via text mining and correspondence analysis;…

Statistical terms are used in everyday language and, at times, used in non-statistical ways. It is often assumed students understand statistical terms because of their common use; however, research into their understanding of specific statistical terms is scant. This report focuses on 58 Year 3 students' responses to the basic question, "What…

This paper depicts a perceptual picture of learning analytics based on the understanding of learners and teachers at the SSU as a case study. The existing literature covers technical challenges of learning analytics (LA) and how it creates better social construct for enhanced learning support, however, there has not been adequate research on…

Higher education administrators lack strong understanding about using course evaluation data to make judgments and decisions. Using archival student course evaluation data from a pharmacy school, we examined (a) course evaluation instrument reliability and validity; (b) bivariate relationships between eight course rating items and the overall…

Teaching an introductory programming course to an average of 40 students while monitoring their performance can be a challenge for instructors. Preparing coding exercises with test cases and checking students' programs can prove to be time consuming at times. Moreover, programming has been known to be quite difficult for students to learn. To…

Large quantities of data are now generated, collated and processed within schools through computerized systems and other digital technologies. In response to growing concerns over the efficiency and equity of how data is used, the concept of 'open data' has emerged as a potential means of democratizing data access and use across school…

Massive Open Online Courses (MOOCs) are widely used to deliver specialized education and training in different fields. Determining the effectiveness of these courses is an integral part of delivering comprehensive, high-quality learning. This study is an evaluation of a MOOC offered by Imperial College London in collaboration with Health iQ…

The results of educational research studies are only as accurate as the data used to produce them. Drawing on experiences conducting large-scale efficacy studies of classroom-based algebra interventions for community college and middle school students, I am developing practice-based data cleaning procedures to support scholars in conducting…

Attributes of teaching and learning contexts provide rich information about how students participate in learning activities. By tracking and analyzing snapshots of these attributes captured continuously throughout the duration of the learning activities, teachers can identify individual students who need special attention and apply different…

An important, yet largely unstudied problem in student data analysis is to detect "misconceptions" from students' responses to "open-response" questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of…

The aim of this study was to explore students' experience and reflection in doing a Service Learning project as part of their course work. The Service Learning project allows the students to practice their knowledge of raising capital through crowdfunding, and at the same time situates them in an environment where they could learn from the…