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Kokoç, Mehmet; Akçapinar, Gökhan; Hasnine, Mohammad Nehal – Educational Technology & Society, 2021
This study analyzed students' online assignment submission behaviors from the perspectives of temporal learning analytics. This study aimed to model the time-dependent changes in the assignment submission behavior of university students by employing various machine learning methods. Precisely, clustering, Markov Chains, and association rule mining…
Descriptors: Electronic Learning, Assignments, Behavior Patterns, Learning Analytics
Chance, Beth; Reynolds, Shea – Journal of Statistics Education, 2019
Through a series of explorations, this article will demonstrate how the Kentucky Derby winning times dataset provides various opportunities for introductory and advanced topics, from data processing to model building. Although the final goal may be a prediction interval, the dataset is rich enough for it to appear in several places in an…
Descriptors: Prediction, Statistics, Data Processing, Homework
Ross, Matthew M.; Wright, A. Michelle – Journal of Education for Business, 2022
We use Markov chain Monte Carlo (MCMC) analysis to construct a three-question math quiz to assess key skills needed for introductory finance. We begin with data collected from a ten-question criterion-referenced math quiz given to 314 undergraduates on the first day of class. MCMC indicates the top three questions for predicting overall course…
Descriptors: Mathematics Tests, Markov Processes, Monte Carlo Methods, Introductory Courses
Westman, Brittainy; Whitworth, Brooke A. – Science and Children, 2019
PEOE (predict, explain, observe, explain) is a strategy that supports conceptual change (Dial et al. 2009). "Conceptual change" is a process through which students can change their understandings, ideas, or beliefs (diSessa 1993; Konicek-Moran and Keeley 2015). This style of lesson allows students to express their scientific ideas…
Descriptors: Science Instruction, Toys, Physics, Scientific Concepts
Thontirawong, Pipat; Chinchanachokchai, Sydney – Marketing Education Review, 2021
In the age of big data and analytics, it is important that students learn about artificial intelligence (AI) and machine learning (ML). Machine learning is a discipline that focuses on building a computer system that can improve itself using experience. ML models can be used to detect patterns from data and recommend strategic marketing actions.…
Descriptors: Marketing, Artificial Languages, Career Development, Time Management
Malin, Heather; Liauw, Indrawati; Remington, Kathleen – Journal of Character Education, 2019
Purpose is an important aspect of character development and thriving in adolescence; yet, there is little research explaining how it develops or how contexts such as school can support its development. In this study, 1,304 eighth graders completed a survey that measured purpose as the integration of 2 dimensions-- beyond-the-self life goal…
Descriptors: Personality, Educational Environment, Grade 8, Student Attitudes
Arteaga Sánchez, Rocío; Cortijo, Virginia; Javed, Uzma – E-Learning and Digital Media, 2019
Social network sites in general, and Facebook in particular, allow users with common interests to meet, share ideas, and collaborate, creating new forms of informal learning. In order to understand and eventually take advantage of the many benefits that Facebook can bring to the academic world, we need to study its adoption process. The objective…
Descriptors: Social Media, Social Networks, Informal Education, Educational Benefits
Williams, Heidi; Burns, Claudia; Daisey, Peggy – Grantee Submission, 2016
(Purpose) The purpose of this paper is to describe visual literacy and an adapted version of Visual Thinking Strategy (VTS) and its value to enhance students' inferential thinking. (Methodology) An example of a middle school VTS, art-integrated lesson is described as well as reflections of a middle school language arts teacher about what she…
Descriptors: Visual Literacy, Learning Strategies, Thinking Skills, Middle School Students
Leiman, Tania; Abery, Elizabeth; Willis, Eileen M. – Journal of University Teaching and Learning Practice, 2015
Research involving student and tutor responses to a "pedagogy of the heart" approach in a first year university health science topic revealed anxiety, insecurity and perceptions of unpredictability in relation to an innovative arts-based assignment designed to elicit and assess experiential or imaginal knowledge. Using the lens of…
Descriptors: Risk, Student Evaluation, Affective Behavior, Emotional Response
Samsa, Gregory P.; Thomas, Laine; Lee, Linda S.; Neal, Edward M. – Journal of Statistics Education, 2012
Clinicians have characteristics--high scientific maturity, low tolerance for symbol manipulation and programming, limited time outside of class--that limit the effectiveness of traditional methods for teaching multi-predictor modeling. We describe an active-learning based approach that shows particular promise for accommodating these…
Descriptors: Active Learning, Statistics, Mathematics Instruction, Prediction
Duijnhouwer, Hendrien; Prins, Frans J.; Stokking, Karel M. – Learning and Instruction, 2012
This study investigated the effects of feedback providing improvement strategies and a reflection assignment on students' writing motivation, process, and performance. Students in the experimental feedback condition (n = 41) received feedback including improvement strategies, whereas students in the control feedback condition (n = 41) received…
Descriptors: Feedback (Response), Self Efficacy, Student Motivation, Writing Processes
Chatterjee, B.; Dey, D.; Chakravorti, S. – IEEE Transactions on Education, 2011
Partial discharge (PD) monitoring is an effective predictive maintenance tool for electrical power equipment. As a result, an understanding of the theory related to PD and the associated measurement techniques is now necessary knowledge for power engineers in their professional life. This paper presents a modular course on PD phenomenon in which…
Descriptors: Foreign Countries, Energy, Equipment, Maintenance
Barnes, Tiffany, Ed.; Chi, Min, Ed.; Feng, Mingyu, Ed. – International Educational Data Mining Society, 2016
The 9th International Conference on Educational Data Mining (EDM 2016) is held under the auspices of the International Educational Data Mining Society at the Sheraton Raleigh Hotel, in downtown Raleigh, North Carolina, in the USA. The conference, held June 29-July 2, 2016, follows the eight previous editions (Madrid 2015, London 2014, Memphis…
Descriptors: Data Analysis, Evidence Based Practice, Inquiry, Science Instruction