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Corcelles, M.; Cano, M.; Liesa, E.; González-Ocampo, G.; Castelló, M. – Higher Education Research and Development, 2019
During their doctoral studies, students undergo an emotionally and intellectually intensive process involving a wide range of positive and negative experiences. This article analyses PhD students' perceptions of the most positive and negative experiences related to doctoral study conditions. Previous researchers have primarily focused on analysing…
Descriptors: Foreign Countries, Doctoral Students, Student Attitudes, Student Experience
Fincham, Ed; Gasevic, Dragan; Jovanovic, Jelena; Pardo, Abelardo – IEEE Transactions on Learning Technologies, 2019
Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather…
Descriptors: Study Habits, Learning Strategies, Independent Study, Educational Research
van Halema, Nicolette; van Klaveren, Chris; Drachsler, Hendrik; Schmitz, Marcel; Cornelisz, Ilja – Frontline Learning Research, 2020
For decades, self-report instruments -- which rely heavily on students' perceptions and beliefs -- have been the dominant way of measuring motivation and strategy use. Event-based measures based on online trace data arguably has the potential to remove analytical restrictions of self-report measures. The purpose of this study is therefore to…
Descriptors: Independent Study, Learning Motivation, Learning Strategies, Student Behavior
Pytlarz, Ian; Pu, Shi; Patel, Monal; Prabhu, Rajini – International Educational Data Mining Society, 2018
Identifying at-risk students at an early stage is a challenging task for colleges and universities. In this paper, we use students' oncampus network traffic volume to construct several useful features in predicting their first semester GPA. In particular, we build proxies for their attendance, class engagement, and out-of-class study hours based…
Descriptors: College Freshmen, Grade Point Average, At Risk Students, Academic Achievement
Gitinabard, Niki; Barnes, Tiffany; Heckman, Sarah; Lynch, Collin F. – International Educational Data Mining Society, 2019
Students' interactions with online tools can provide us with insights into their study and work habits. Prior research has shown that these habits, even as simple as the number of actions or the time spent on online platforms can distinguish between the higher performing students and low-performers. These habits are also often used to predict…
Descriptors: Blended Learning, Student Adjustment, Online Courses, Study Habits
Veletsianos, George; Reich, Justin; Pasquini, Laura A. – AERA Open, 2016
Big data from massive open online courses (MOOCs) have enabled researchers to examine learning processes at almost infinite levels of granularity. Yet, such data sets do not track every important element in the learning process. Many strategies that MOOC learners use to overcome learning challenges are not captured in clickstream and log data. In…
Descriptors: Data Analysis, Data Collection, Online Courses, Learning Strategies
Wan, Han; Liu, Kangxu; Yu, Qiaoye; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2019
Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning…
Descriptors: Educational Practices, Learner Engagement, Identification, Study Habits
Caprotti, Olga – Journal of Learning Analytics, 2017
This paper describes investigations in visualizing logpaths of students in an online calculus course held at Florida State University in 2014. The clickstreams making up the logpaths can be used to visualize student progress in the information space of a course as a graph. We consider the graded activities as nodes of the graph, while information…
Descriptors: Online Courses, Calculus, Markov Processes, Graphs
Kumar, Satish; Jena, Lingaraja; Vagha, Jayant – Biochemistry and Molecular Biology Education, 2016
In order to review the need assessment of enhancing the weightage of Applied Biochemistry in the undergraduate curriculum at Mahatma Gandhi Institute of Medical Sciences (MGIMS), Sevagram, a validated questionnaire was sent to 453 participants which include 387 undergraduate students, 11 interns, 23 postgraduate students, and 32 faculty members. A…
Descriptors: Needs Assessment, Biochemistry, Undergraduate Students, Medical Schools
Dvorak, Tomas; Jia, Miaoqing – Journal of Learning Analytics, 2016
This study analyzes the relationship between students' online work habits and academic performance. We utilize data from logs recorded by a course management system (CMS) in two courses at a small liberal arts college in the U.S. Both courses required the completion of a large number of online assignments. We measure three aspects of students'…
Descriptors: Online Courses, Educational Technology, Study Habits, Academic Achievement
Baharev, Zulejka – ProQuest LLC, 2016
At the start of the 21st century large scale educational initiatives reshaped the landscape of general education setting rigorous academic expectations to all students. Despite the legal efforts to improve K-12 education, an abundance of research indicates that students entering college often lack basic learning and study skills. For adolescents…
Descriptors: Notetaking, Learning Strategies, Recall (Psychology), Comprehension
Miyamoto, Yohsuke R.; Coleman, Cody A.; Williams, Joseph Jay; Whitehill, Jacob; Nesterko, Sergiy; Reich, Justin – Journal of Learning Analytics, 2015
A long history of laboratory and field experiments have demonstrated that dividing study time into many sessions is often superior to massing study time into few sessions, a phenomenon known as the "spacing effect." We use this well-established finding from the psychology literature as inspiration for investigating how students…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
Pardos, Zachary A. – Journal of Learning Analytics, 2015
In Miyamoto et al. (2015, this issue) the authors looked to substantiate the presence of the spacing effect, referenced from the psychology literature, in several MOOCs. Their secondary analyses constituted a robust, empirical finding on the correspondence between session distribution and certification but with only a coarse, analogous…
Descriptors: Online Courses, Large Group Instruction, Educational Technology, Technology Uses in Education
Chen, Xin; Vorvoreanu, Mihaela; Madhavan, Krishna – IEEE Transactions on Learning Technologies, 2014
Students' informal conversations on social media (e.g., Twitter, Facebook) shed light into their educational experiences--opinions, feelings, and concerns about the learning process. Data from such uninstrumented environments can provide valuable knowledge to inform student learning. Analyzing such data, however, can be challenging. The complexity…
Descriptors: Social Media, Data Analysis, Sleep, Engineering Education
Wright, Mary C.; McKay, Timothy; Hershock, Chad; Miller, Kate; Tritz, Jared – Change: The Magazine of Higher Learning, 2014
Learning Analytics (LA) has been identified as one of the top technology trends in higher education today (Johnson et al., 2013). LA is based on the idea that datasets generated through normal administrative, teaching, or learning activities--such as registrar data or interactions with learning management systems--can be analyzed to enhance…
Descriptors: STEM Education, Introductory Courses, Physics, Technology Uses in Education
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