experts' corner: online students
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Learn the latest about learner motivation, affect, computer and internet skills, and more.
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Dr. Richard Mayer introduces a special section on multimedia learning with this commentary on how effective multimedia can increase motivation without distracting from learning.
added June 24, 2014 "Incorporated Motivation into Multimedia Learning." Mayer, Richard E. (2014) Learning and Instruction 29:171-173. After integrating several compatible views on how multimedia can interfere or enhance learning, the author concludes that, "...motivational features can improve student learning by fostering generative processing as long as the learner is not continually overloaded with extraneous processing or overly distracted from essential processing." Go to original article (may require journal subscription)... |
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Researchers use large data set to assess how well college students perform in online courses versus face-to-face courses.
added June 24, 2014 "Adaptability to Online Learning: Differences Across Types of Students and Academic Subject Areas" Xu, Di, and Shanna Jaggars (2013) Using a data set of almost 500,000 students at over 40,000 community and technical colleges, this study finds that while all students did worse in online courses, males, younger students, black students, and students with lower grade point averages suffered the most relative to their level of success in face-to-face courses. Go to original article (may require journal subscription)... |
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Researchers find "math achievement emotions" are the best predictors of math achievement for virtual high school students.
added June 22, 2014 "Affective and motivational factors of learning in online mathematics courses." Kim, ChanMin, Seung Won Park, and Joe Cozart (2014) British Journal of Educational Technology 45(1):171-185. Researchers looked at the impact of motivation (self-efficacy and intrinsic value), "mathematics achievement emotions" (anxiety, anger, shame, hopelessness, boredom, enjoyment, and pride), and cognitive processes (cognitive strategy used and self-regulation) on achievement in students studying math at a virtual high school. They initially found that motivation accounted for ~13% of the variance in student achievement and self-efficacy was the significant individual predictor of student achievement. But, when math achievement emotions were added to the analysis, self-efficacy failed to predict student achievement, while the emotions accounted for 37% of the variance. They also found that cognitive strategy use and self-regulation did not explain any additional variance in achievement. Go to original article (may require journal subscription)... |
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Researchers identify what's important for student satisfaction in college-level online courses.
added June 21, 2014 "Interaction, Internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses." Kuo, Yu-Chun, Andrew E. Walker, Kerstin EE Schroder, and Brian R. Belland (2014) The Internet and Higher Education 20:35-50. Researchers surveyed graduate and undergraduate students with questions distance education courses. Then they modeled the results considering three types of interactions, internet self-efficacy, self-regulated learning, course category, and academic program. They found that Learner–instructor interaction and learner–content interaction were significant predictors of student satisfaction but learner–learner interaction was not. Go to original article (may require journal subscription)... |