Using data from educational technologies to improve the student experience of blended learning
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This document discusses how data from educational technologies can be used to improve the blended learning student experience. It defines blended learning and educational technologies. Data can be used to provide actionable information on student performance, improve participation and collaboration, personalize learning pathways, gauge content engagement to improve teaching materials, and improve accessibility of digital content. Considerations for learning analytics include the need for human interpretation of data and ensuring student privacy.
Using data from educational technologies to improve the student experience of blended learning
- 1. HOW WOULD YOU USE DATA GATHERED FROM EDUCATIONAL TECHNOLOGIES TO IMPROVE THE STUDENT EXPERIENCE OF BLENDED LEARNING?
- 2. DEFINITIONS Blended Learning (Garrison et al, 2011) • Thoughtfully integrating face-to-face and online learning • Fundamentally rethinking the course design to optimize student engagement • Restructuring and replacing traditional class contact hours • Learners as (active) contributors to knowledge, not just (passive) consumers Educational Technologies
- 3. ENHANCING STUDENT ACHIEVEMENT AND SUCCESS Providing actionable information about student performance Improving participation and collaboration Personalisation of learning pathways Gauging content engagement and improving teaching materials Improving accessibility of digital course content
- 4. PROVIDING ACTIONABLE INFORMATION ABOUT STUDENT PERFORMANCE Analytics Insights •- Overview of Completion rates, Students' progress, When a user last logged in, Course status, Number of students enrolled, Total time spent in the LMS, Individual assessment/quiz scores and answers, Learning path data (how far in the course a learner is), Number of answer attempts •- Identifying students who appear less likely to succeed academically or are at rick of dropping out •- Course leaders can connect with students via the platform to encourage and provide assistance (targeted interventions) •- Identifying specific units of study or assessment in a course that cause difficulty Image from https://moodle.com/news/moodle-learning- analytics-increase-student-engagement/
- 5. IMPROVING PARTICIPATION AND COLLABORATION Summary report Valuable data relating to forum activity - Number of discussions started - Replies they’ve made - Earlier and most recent posts Extension to classroom discussions Enhancing student agency Image from https://moodle.com/news/moodle- learning-analytics-increase-student-engagement/
- 6. PERSONALISATION OF LEARNING PATHWAYS Determining student needs and suggesting tailored study plans Activity chooser which makes it easier to locate activities and resources. A new ‘Starred’ tab lets you favourite your most used items, Moodle administrators can recommend activities and resources for teachers in the new ‘Recommended’ tab. • Enabling adaptive learning help students to develop skills and knowledge in a more personalized and self-paced way. • Learning Analytics enables the educational content to be tailored to the adequate level of understanding as they progress through it (scaffolding, unlocking activities, different types of activities)
- 7. GAUGING CONTENT ENGAGEMENT AND IMPROVING TEACHING MATERIALS • Audience Analytics - How users consume and engage with content? • Contributors - Who are the users that are creating content? and with what tools? • Usage - System reports including BW, storage and transcoding consumption • Real-time Analytics Next Generation - Dashboard of analytics for live events in the past 7 days • Entry Level Analytics - Dashboard analysing a specific entry • User Analytics - Dashboard providing a view of a user’s activities, including highlights, insights, and details of engagement and contribution. • Category Analytics - Dashboard containing data analysis about a category. Tracking and analytics after each Virtual Classroom class/meeting: • Quiz Scores - Detailed view for each question/answer, as well as the overall score. • Attendance data - Time joined, left, and total duration. • Attention/Focus - Average time the browser was in focus. • Chat history Image source: https://corp.kaltura.com/blog/get-actionable-insights-from-your-video-data-with-new-kaltura-video-analytics/
- 8. IMPROVING ACCESSIBILITY OF DIGITAL COURSE CONTENT Inclusive learning experience Providing accommodations for students with disabilities Providing alternative formats for more accessible course content •HTML •Audio Pub •electronic Braille •tagged PDF Feedback on accessibility of course content Detailed guidance on how to fix accessibility issues
- 9. LEARNING ANALYTICS FOR HEIS • Descriptive – performance positioning, engagement, impact of pedagogy via assessment results, time allocated to achieve a specific activity, drop out rates • Diagnostic – analysis and comparison of live learning paths • Predictive – allows the student to gauge own achievement, determining students at risk, intervention while it could still impact success • Prescriptive – drawing a personalized learning plan, suggesting relevant activities, identifying trends in dropouts
- 10. CONSIDERATIONS (WHAT LEARNING ANALYTICS CANNOT DO) • Data from tracking systems is not inherently intelligent or sufficient in itself, it needs a broader (human) interpretation (subject, students’ age, background, class attendance) • Privacy – using data responsibly and avoiding the Big Brother effect • Learning Analytics can point to areas in need of improvement and identify engaging practices, but cannot make suggestions for improvements.
- 11. RESOURCES Cabaleiro-Cerviño, Goretti and Vera, Carolina (2020) ‘The Impact of Educational Technologies in Higher Education’, GIST Education and Learning Research Journal, 20, pp. 155–169. doi: 10.26817/16925777.711. FitzGerald (2020) ‘The promises and pitfalls of personalised eLearning’, in. Belgrade Metropolitan University. Garrison, D. Randy and Vaughan, Norman D (2011) Blended Learning in Higher Education. 1. Aufl. New York, NY: Jossey-Bass. Wankel, C. and Blessinger, Patrick (2013) Increasing student engagement and retention in e-learning environments : Web 2.0 and blended learning technologies. Bradford: Emerald Group Publishing Limited. https://www.blackboard.com/en-uk/teaching-learning/accessibility-universal-design/blackboard-ally-lms https://moodle.com/news/moodle-learning-analytics-increase-student-engagement/ https://www.slideshare.net/rscapin/how-to-use-learning-analytics-in-moodle/19-Learning_AnalyticsThe_most_common_use