1. The document discusses an experiment using interactive programming tasks during learning videos in a MOOC course on Python basics to test their impact on learning effectiveness.
2. 577 learners participated in the course and were split into two groups - one with programming overlays and one without. Quiz results showed the group with overlays performed better, indicating the overlays could influence learning.
3. Data from the programming tasks and interactive platform provided additional insights. Task results showed some concepts were challenging and attention to accuracy is important. Statistics on learner behavior can help optimize task placement and video content.
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Presented at ACM Learning at Scale 2014, March 4-5 2014, Atlanta, GA, USA.
Computing Student Success at Montgomery College in the Web 3.0 Eraafacct
Computing Student Success at Montgomery College (MC) in Maryland is deeply rooted to the Web 3.0 era. The success of the Computer Science and Information System students at MC has evolved over time. The various success stories of the Montgomery College students were presented, and the innovative pedagogy that the faculty are using at MC in this Web 3.0 era was explored. Off-course, the rapid and efficient communication among the faculty members, and also among faculty members and the student body was made possible due to the innovative technologies that the Web 3.0 has to offer. Besides, the student success at MC is deeply rooted to the inter-faculty co-operations, and collaborations in and outside of the discipline. Attendees discussed contributions of the Web 3.0 technologies to the Student Success at other institutions as well. As a result, the overall discussion extended to various Maryland institutions besides only the Montgomery College. Faculty attending the session explored innovative, and active learning strategies made possible through Web 3.0. They discussed future undertakings that could have been possible through Web 3.0, and would accelerate the traditionalistic means of pedagogical delivery.
Understanding In-Video Dropouts and Interaction Peaks in Online Lecture VideosJuho Kim
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Presented at ACM Learning at Scale 2014, March 4-5 2014, Atlanta, GA, USA.
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Context: One limitation of the empirical studies about test- driven development (TDD) is knowing whether the developers followed the advocated test-code-refactor cycle. Re- search dealt with the issue of process conformance only in terms of internal validity, while investigating the role of other confounding variables that might explain the contro- versial e↵ects of TDD. None of the research included process conformance as a fundamental part of the analysis.
Goal: We aim to examine the impact of process conformance on the claimed effects of TDD on external quality, developers’ productivity and test quality.
Method: We used data collected during a previous study to create regression models in which the level of process con- formance was used to predict external quality, productivity, and tests thoroughness.
Result: Based on our analysis of the available data (n = 22), we observe that neither quality (p value = 0.21), produc- tivity (p value = 0.80), number of tests (p value = 0.39) nor coverage (p value = 0.09) was correlated with the level of TDD process conformance.
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The use of programming tasks in interactive videos to increase learning effectiveness at MOOCs
1. SCIENCE
PASSION
TECHNOLOGY
The use of programming tasks in interactive videos to
increase learning effectiveness at MOOCs
Michael Weiß
23. November 2023
1
2. Introduction
• Motivation
§ Emergence of videos
§ Importance of interactivity in videos
§ Bachelor Thesis: Game-based learning with Alexa
Ø iMooX course with programming overlays during learning videos
2
3. Research Question
Can interactive programming tasks during learning videos support
learning efficiency and success?
• How to develop interactive programming tasks during learning videos for an
interactive video platform?
• What conclusions can be drawn from user behaviour for the optimization of the
course and interactive elements?
3
4. Content
1. Interactive video course (MOOC)
2. Programming task overlays
3. Evaluation & Outcome of course results
4
5. Content
1. Interactive video course (MOOC)
2. Programming task overlays
3. Evaluation & Outcome of course results
5
6. 1. Interactive video course (MOOC)
• iMooX Course – Python basics
§ Part 1: Programming & Basics
§ Part 2: Python Basics
§ Part 3: Input / Output of data & Control flow
§ Part 4: Lists & List functions
• Two test groups
§ Group 1: 22. March - 15. May -> watched with overlays
§ Group 2: 16. May - 14. August -> watched without overlays
• 577 learners
6
7. Course procedure
• Beginning of the course
§ Self-evaluation questionnaire
• For each part of the course
§ Self-assessment quiz before the part (5 MC-Questions)
§ Video
§ Group 1: With programming overlays
§ Group 2: Without programming overlays
§ Final quiz ( 5 MC-Questions)
• Exception: Part 1 – no programming overlays
7
9. Content
1. Interactive video course (MOOC)
2. Programming task overlays
3. Evaluation & Outcome of course results
9
10. 2. Programming task overalys
• Limitation with iMooX -> use LIVE
• Communication between iMooX, LIVE
and Youtube
• LIVE
§ Django – Python Webframework
§ Interaction: Programming Task
10
Figure: High-level architecture of LIVE
11. Interaction: Programming tasks
• Functionalities
§ Planning, Creating and editing tasks
§ Display tasks during videos
§ Safe execution and evaluation of code answers
§ Teacher area to display results
§ Learner area to display results
§ Download of programming tasks metadata, statistics and answers
11
12. Interaction: Programming tasks
• Functionalities
§ Planning, Creating and editing tasks
§ Display tasks during videos
§ Safe execution and evaluation of code answers
§ Teacher area to display results
§ Learner area to display results
§ Download of programming tasks metadata, statistics and answers
12
13. Plan & Create a programming task
• Teacher area
• Choose timestamp for task display
• Insert description, answer, reference output
13
14. Interaction: Programming tasks
• Functionalities
§ Planning, Creating and editing tasks
§ Display tasks during videos
§ Safe execution and evaluation of code answers
§ Teacher area to display results
§ Learner area to display results
§ Download of programming tasks metadata, statistics and answers
14
15. Display of tasks during videos
• Part 2:
§ multiplication and
variable assignment
• Part 3:
§ Simple division and
If-else statement
• Part 4:
§ List creation and
iteration
15
16. Interaction: Programming tasks
• Functionalities
§ Planning, Creating and editing tasks
§ Display tasks during videos
§ Safe execution and evaluation of code answers
§ Teacher area to display results
§ Learner area to display results
§ Download of programming tasks metadata, statistics and answers
16
17. Safe execution and evaluation of code answers
• Restricted Python Package (malicious code)
§ Define a subset of the python language
§ E.g. block import of modules, allow only certain elements
§ Execution time limit of 2 seconds
§ Execute not trusted code inside a trusted environment
17
18. Interaction: Programming tasks
• Functionalities
§ Planning, Creating and editing tasks
§ Display tasks during videos
§ Safe execution and evaluation of code answers
§ Teacher area to display results
§ Learner area to display results
§ Download of programming tasks metadata, statistics and answers
18
22. Content
1. Interactive video course (MOOC)
2. Programming task overlays
3. Evaluation & Outcome of course results
22
23. 3. Evaluation & Outcome
1. iMooX data
• Quiz results
2. LIVE data
• Programming task results
• User statistics
§ By Daniel Dohr (Visualization of students’ behavior in interactive learning videos)
23
24. 3. Evaluation & Outcome
1. iMooX data
• Quiz results
2. LIVE data
• Programming task results
• User statistics
§ By Daniel Dohr (Visualization of students’ behavior in interactive learning videos)
24
25. iMooX Data – Quiz results
25
Part 1 – Theory: No overlays
Part 2 – Python basics
Part 3 – Input / Output Control flow
Part 4 – List / List functions
G1 – watched with overlays
G2 – watched without overlays
26. Quiz results – conclusion
• Group 1 performed better than Group 2 on final quizzes
ü Programming overlays could influence learning efficiency
§ Limitation: Smaller test group 2 limits expressiveness of results
• Both groups improved from the initial assessments to final quizzes
ü Success of the course
26
27. 3. Evaluation & Outcome
1. iMooX data
§ Quiz results
2. LIVE data
§ Programming task results
§ User statistics
§ By Daniel Dohr (Visualization of students’ behavior in interactive learning videos)
27
28. 3. Evaluation & Outcome
1. iMooX data
§ Quiz results
2. LIVE data
§ Programming task results
§ User statistics
§ By Daniel Dohr (Visualization of students’ behavior in interactive learning videos)
28
29. Programming task results
• Task 1
§ mixing of addition
and multiplication
• Task 2 & Task 3
§ Inaccuracy & failure to
understand the topic
• Conclusion
• Simple mathematical operations must not be considered as pre-existing
knowledge
• importance of working accurately
29
61 %
Correct %
34 %
45 %
30. 3. Evaluation & Outcome
1. iMooX data
§ Quiz results
2. LIVE data
§ Programming task results
§ User statistics
§ By Daniel Dohr (Visualization of students’ behavior in interactive learning videos)
30
31. LIVE data
• User statistics during learning videos
§ Answer Delay
§ Attention Change
§ Drop ratio
31
32. LIVE data
• User statistics during learning videos
§ Answer Delay
§ Attention Change
§ Drop ratio
32
36. LIVE data
• User statistics during learning videos
§ Answer Delay
§ Attention Change
§ Drop ratio
36
37. Drop ratio
• Sharp drops in
part 1 & 2
• Constant numbers in
part 3 & 4
• Assumptions
§ Lost interest
§ Get taste of course
§ Too complex
§ Improve videos
37
38. Conclusion
• Success of the course
• Programming tasks as a valuable addition
• Further research
§ Equal sized test group
§ Special characteristics of programming tasks
38