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Vincent Aleven; Jori Blankestijn; LuEttaMae Lawrence; Tomohiro Nagashima; Niels Taatgen – Grantee Submission, 2022
Past research has yielded ample knowledge regarding the design of analytics-based tools for teachers and has found beneficial effects of several tools on teaching and learning. Yet there is relatively little knowledge regarding the design of tools that support teachers when a class of students uses AI-based tutoring software for self-paced…
Descriptors: Educational Technology, Artificial Intelligence, Problem Solving, Intelligent Tutoring Systems
Thomas Qiao; Brenda McDermott; Jennifer E. Thannhauser – Papers on Postsecondary Learning and Teaching, 2024
Responses to the transition to online learning during the pandemic underscores the importance of faculty engagement in professional development (PD) to enhance their teaching practices. However, the creation and offering of PD opportunities does not always lead to faculty engagement. Using a change management perspective (the ADKAR framework),…
Descriptors: Faculty Development, Learner Engagement, Teaching Methods, Facility Improvement
Chung, Cheng-Yu; Hsiao, I-Han – International Educational Data Mining Society, 2021
The distributed practice effect suggests that students retain learning content better when they pace their practice over time. The key factors are practice dosage (intensity) and timing (when to practice and how in between). Inspired by the thriving development of image recognition, this study adopts one of the successful techniques,…
Descriptors: Models, Drills (Practice), Pacing, Computer Uses in Education
Howlin, Colm P.; Dziuban, Charles D. – International Educational Data Mining Society, 2019
Clustering of educational data allows similar students to be grouped, in either crisp or fuzzy sets, based on their similarities. Standard approaches are well suited to identifying common student behaviors; however, by design, they put much less emphasis on less common behaviors or outliers. The approach presented in this paper employs fuzzing…
Descriptors: Data Collection, Student Behavior, Learning Strategies, Feedback (Response)
Mao, Ye; Marwan, Samiha; Price, Thomas W.; Barnes, Tiffany; Chi, Min – International Educational Data Mining Society, 2020
Modeling student learning processes is highly complex since it is influenced by many factors such as motivation and learning habits. The high volume of features and tools provided by computer-based learning environments confounds the task of tracking student knowledge even further. Deep Learning models such as Long-Short Term Memory (LSTMs) and…
Descriptors: Time, Models, Artificial Intelligence, Bayesian Statistics
Lane, Mathew; Hui, Wendy; Murcia, Karen; Wongthongtham, Pornpit – International Association for Development of the Information Society, 2019
This article is an exploratory analysis and comparison of the demographic distributions of data collected from the 2016 New Coder Survey, with that obtained from the Integrated Postsecondary Education Data System (IPEDS). In comparing the data sets, the findings suggest that overall females were more likely to engage in online self-paced coding…
Descriptors: Electronic Learning, Coding, Computer Science Education, Postsecondary Education
Toyooka, Hiroshi; Matsuura, Kenji; Gotoda, Naka – International Association for Development of the Information Society, 2016
In the learning support for repetitive motions having an operating instrument, it is necessary for learners to control not only their own body motions but also an instrument corresponding to the body. This study focuses on the repetitive motion learning using single operation instrument without the movement in space; i.e. jump-rope and hula-hoop.…
Descriptors: Motion, Repetition, Learning Processes, Skill Development
Interactive Video, Tablets and Self-Paced Learning in the Classroom: Preservice Teachers Perceptions
Papadopoulou, Anthia; Palaigeorgiou, George – International Association for Development of the Information Society, 2016
In recent years, a lot of focus has been given to the study of interactive video. However, interactive video has not been examined as a tool for self-directed learning in the classroom and has not been exploited together with tablets. This study tries to assess the value of an e-learning environment which is based primarily on interactive learning…
Descriptors: Interactive Video, Handheld Devices, Individualized Instruction, Pacing
Toward a Real-Time (Day) Dreamcatcher: Sensor-Free Detection of Mind Wandering during Online Reading
Mills, Caitlin; D'Mello, Sidney – International Educational Data Mining Society, 2015
This paper reports the results from a sensor-free detector of mind wandering during an online reading task. Features consisted of reading behaviors (e.g., reading time) and textual features (e.g., level of difficulty) extracted from self-paced reading log files. Supervised machine learning was applied to two datasets in order to predict if…
Descriptors: Reading, Identification, Attention, Reading Rate
Lyddon, Paul A. – European Association for Computer-Assisted Language Learning (EUROCALL), 2012
Acquiring sufficient linguistic proficiency to perform competently in academic and professional contexts generally requires substantial study time beyond what most language programs can offer in the classroom. As such, teachers
and students alike would benefit considerably from high quality self-access materials promoting independent learning out…
Descriptors: Grammar, Second Language Instruction, Independent Study, Computer Assisted Instruction
Hershkovitz, Arnon; Nachmias, Rafi – International Working Group on Educational Data Mining, 2009
The purpose of this study is to investigate the consistency of students' behavior regarding their pace of actions over sessions within an online course. Pace in a session is defined as the number of logged actions divided by session length (in minutes). Log files of 6,112 students were collected, and datasets were constructed for examining pace…
Descriptors: Foreign Countries, Psychometrics, Comparative Analysis, Correlation
Higginbotham-Wheat, Nancy – 1990
This review of the literature on five learner control variables in computer-based instruction concludes that: (1) pacing left to learner control has been found ineffective because learners tend to procrastinate or to exit lessons prematurely; (2) sequence, or content flow, has been an ineffective variable left under learner control since…
Descriptors: Computer Assisted Instruction, Difficulty Level, Learner Controlled Instruction, Pacing
Leppmann, P. K.; Herrmann, T. F. – 1981
The effects of distributed study, self-pacing, a criterion of mastery, and individualized testing were examined by assigning 422 students in an introductory psychology course to one of four different teaching/learning systems. The first encompassed a traditional lecture/discussion group format. The second employed the personalized system of…
Descriptors: Individual Testing, Intermode Differences, Lecture Method, Mastery Learning
Beyerbach, Barbara A. – 1989
This study examined the interrelationships between research, policy, and practice. A year-long observation study was made of the lives and activities of a group of educational researchers collaborating on a nationally funded research investigation. The researchers were trying to implement wait time in high school biology and chemistry classes.…
Descriptors: High Schools, Interaction Process Analysis, Pacing, Research
Ruthven, Avis J. – 1984
The effects of two types of contingency contracts and self-pacing on student performance in a personalized system of instruction (PSI) class were examined. Forty-seven college students in a principles of educational psychology class were randomly assigned to either a short contract, long contract or self-pacing group. One group of students signed…
Descriptors: Academic Achievement, College Students, Comparative Analysis, Contingency Management