A multivariate approach to predicting student outcomes in web-enabled blended learning courses

By Nick Z. Zacharis. Internet and Higher Education, 2015

296 citations recorded by the source corpus.

Subfields: Computer Science Applications, Developmental and Educational Psychology, Education

View Paper: https://www.semanticscholar.org/paper/d24a29fef030fa2195fd1f9e933bf07b188773a9

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In Computer Science Applications

In Developmental and Educational Psychology

In Education

Citations in the corpus

Learning Analytics for Learning Design: A Systematic Literature Review of Analytics-Driven Design to Enhance Learning (2019, background): “What is really interesting to be further applied at the intersection of learning analytics and learning design, is finding meaningful behavior patterns and interactions that can be “mapped back to the planned learning activities to explain why certain peaks and falls occur over time” [42], [83], [10”
Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS (2017, methodology): “2 Total time online (min) [13], [28], [31], [32], [37], [39] 4,989 815 678 Number of course page views [32], [33] 4,989 208 144 Irregularity of study time [31], [42], [43] 4,989 1,926 993 Irregularity of study interval 4,989 309,000 252,000 Largest period of inactivity (min) 4,989 20,500 13,100 Time”
Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II (2019, unknown): “Studies like [27,28,30] suggested how different behavioral signals observed in LMS, such as frequency or length of interactions, can be significant predictors of student success in higher education courses.”
What Do We Mean by Blended Learning? (2019, background): “…integrate different instructional methods (Alammary et al. 2014), employ active learning strategies and a variety of pedagogical approaches (Zacharis 2015), include both asynchronous and synchronous online learning (Diep et al. 2017), provide access to rich learning materials and course…”
The impact of a flipped classroom design on learning performance in higher education: Looking for the best "blend" of lectures and guiding questions with feedback (2017, background): “…these modalities (see e.g., Bliuc, Goodyear, & Ellis, 2007; Garrison & Kanuka, 2004; Owston, York, & Murtha, 2013; Vaughan & Garrison, 2005; Zacharis, 2015), research rarely addresses the precise nature of these “blends” and how different “blends” could have a differential impact on…”
Challenges in the online component of blended learning: A systematic review (2020, background): “Zacharis (2015) mentioned that students' technological illiteracy and poor time management skills have led to delays in receiving immediate feedbacks from their teachers, thereby rendering students uninterested and procrastinating their study activities.”
Predicting Student Performance Using Data Mining and Learning Analytics Techniques: A Systematic Literature Review (2020, unknown): “3% [64]) and acceptable predictions (e.”
Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores (2018, background): “Table 1 shows that the number of clicks on glossary and dataplus were unrelated to the student level of engagement in VLE activities [80].”