A brief overview of the topic which I prepared for an interview.

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