How to Uninstall a Module in Odoo 17 Using Command Line
Educational Technologies: Learning Analytics and Artificial Intelligence
1. Educational Technologies:
Learning Analytics
and Artificial Intelligence
Xavier Ochoa
Assistant Professor of Learning Analytics
Learning Analytics Research Network (LEARN)
School of Culture, Education and Human Development
New York University (NYU)
8. Freeman, A., Adams Becker, S., Cummins, M., Davis, A., and Hall Giesinger, C. (2017). NMC/CoSN Horizon Report: 2017 K–12 Edition.
Austin, Texas: The New Media Consortium.
Bryan Alexander, Kevin Ashford-Rowe, Noreen Barajas-Murphy, Gregory Dobbin, Jessica Knott, Mark McCormack, Jeffery Pomerantz, Ryan
Seilhamer, and Nicole Weber, EDUCAUSE Horizon Report: 2019 Higher Education Edition (Louisville, CO: EDUCAUSE, 2019).
9. Freeman, A., Adams Becker, S., Cummins, M., Davis, A., and Hall Giesinger, C. (2017). NMC/CoSN Horizon Report: 2017 K–12 Edition.
Austin, Texas: The New Media Consortium.
Bryan Alexander, Kevin Ashford-Rowe, Noreen Barajas-Murphy, Gregory Dobbin, Jessica Knott, Mark McCormack, Jeffery Pomerantz, Ryan
Seilhamer, and Nicole Weber, EDUCAUSE Horizon Report: 2019 Higher Education Edition (Louisville, CO: EDUCAUSE, 2019).
10. Learning Analytics
Learning analytics is the measurement,
collection, analysis and reporting of data
about learners and their contexts, for
purposes of understanding and
optimizing learning and the environments in
which it occurs.
Society for Learning Analytics Research (SoLAR)
11.
12. Sensemaking
“Sensemaking is a
motivated, continuous
effort to understand
connections . . . in order
to anticipate their
trajectories and act
effectively”
Klein, Gary, Brian Moon, and Robert R. Hoffman. "Making sense of
sensemaking 1: Alternative perspectives." IEEE intelligent systems 21.4
(2006): 70-73.
14. Maier, Karin, Philipp Leitner, and Martin Ebner. "Learning Analytics Cockpit for MOOC Platforms." Emerging Trends in Learning Analytics. Brill Sense, 2019.
271-285.
15. Maier, Karin, Philipp Leitner, and Martin Ebner. "Learning Analytics Cockpit for MOOC Platforms." Emerging Trends in Learning Analytics. Brill Sense, 2019.
271-285.
16. Maier, Karin, Philipp Leitner, and Martin Ebner. "Learning Analytics Cockpit for MOOC Platforms." Emerging Trends in Learning Analytics. Brill Sense, 2019.
271-285.
17. Siemens, George, and Phil Long. "Penetrating the fog: Analytics in learning
and education." Educause Review 46.5 (2011): 30-32.
18. An (simple) example:
Computer Science Program Redesign
Mendez, G., Ochoa, X., Chiluiza, K., & De Wever, B. (2014). Curricular design analysis: a data-driven perspective. Journal
of Learning Analytics, 1(3), 84-119.
20. Which are the hardest/more difficult courses?
What lead our students to success/failure?
How courses are related?
Are there courses that could be eliminated?
Is the work-load adequate for our students?
21. Good Old Academic Data
Student Course Section Semester Grade
9093233 HCD001 1 2005-1S 85
9093233 LMS003 2 2005-1S 97
9088442 HCD001 2 2005-2S 100
… … … … …
26. Difficult Courses (Top 10)
Perceived Estimated
Algorithms Analysis
Operating Systems
Physics A
Differential Equations
Linear Algebra
Programming Fundamentals
Object-Oriented Programming
Differential Calculus
Data Structures
Statistics
Operating Systems
Statistics
Differential Equations
Linear Algebra
Programming Languages
Electrical Networks I
Artificial Intelligence
Programming Fundamentals
Data Structures
Hardware Architecture and Organization
27. Difficult Courses (Top 10)
Perceived Estimated
Algorithms Analysis
Operating Systems
Physics A
Differential Equations
Linear Algebra
Programming Fundamentals
Object-Oriented Programming
Differential Calculus
Data Structures
Statistics
Operating Systems
Statistics
Differential Equations
Linear Algebra
Programming Languages
Electrical Networks I
Artificial Intelligence
Programming Fundamentals
Data Structures
Hardware Architecture and Organization
30. CORE - CS CURRICULUM
Basic Physics
Integral Calculus
Multivariate Calculus
Electrical Networks
Digital Systems I
Hardware Architectures
Operative Systems
General Chemistry
Programming
Fundamentals
Object-oriented
Programming
Data Structures
Programming
Languages
Database Systems I
Software Engineering I
Software Engineering II
Oral and Written
Communication Techniques
Computing and Society
Discrete Mathematics
Algorithms Analysis
Human-computer
Interaction
Differential Calculus
Linear Algebra
Differential Equations
Ecology and
Environmental Education
Statistics
Economic Engineering I
Artificial Intelligence
PROFESSIONAL TRAINING HUMANITIES BASIC SCIENCE
32. UNDERLYING STRUCTURE
Electrical
Networks
Differential
Equations
Software Engineering II
Software Engineering I
HCI
Oral and Written
Communication Techniques
General Chemistry
Programming
Languages
Object-Oriented
Programming
Data Structures
Artificial Intelligence
Operative Systems
Software Engineering
Object-Oriented Programming
Economic Engineering
Hardware Architectures
Database Systems
Digital Systems I
HCI
Differential and Integral Calculus
Linear Algebra
Multivariate Calculus
Digital Systems I
Basic Physics
Programming Fundamentals
Discrete Mathematics
General Chemistry
Statistics
Data Structures
Computing and Society
Algorithms Analysis
Differential Equations
Ecology and Environmental Education
Object-Oriented Programming
FACTOR 1: The basic training factor
FACTOR 2: The advanced
CS topics factor
FACTOR 3: The client
interaction factor
FACTOR 4: The
programming factor
FACTOR 5: The ? factor
33. Grouping is off
Fundamental Programming is not in the Programming factor?
What to do with Electrical Networks and Differential
Equations?
42. Unrealistic Suggested Load
How to the present the Curriculum in a better way?
How we can recommend students the right load?
43. Which are the hardest/more difficult courses?
What lead our students to success/failure?
How courses are related?
Are there courses that could be eliminated?
Is the work-load adequate for our
students?
44. What makes a course difficult then?
Why Programming Fundamentals does not correlate?
Why Computers and Society correlates with a lot of courses?
Fundamental Programming is not in the Programming
factor?
Should students start with CS topics?
Too much pressure in engineering courses?
How to the present the Curriculum in a better way?
How we can recommend students the right load?
What to do with Electrical Networks and Differential
Equations?
45. Good Old Academic Data
Student Course Section Semester Grade
9093233 HCD001 1 2005-1S 85
9093233 LMS003 2 2005-1S 97
9088442 HCD001 2 2005-2S 100
… … … … …
46. Another example:
Augmenting Academic Advising
Gutiérrez, F., Seipp, K., Ochoa, X., Chiluiza, K., De Laet, T., & Verbert, K. (2018). LADA: A learning analytics dashboard for
academic advising. Computers in Human Behavior
47. How to better recommend
academic load to different
students
In 15 minutes or less!
55. Artificial Intelligence
The theory and development of computer
systems able to perform tasks normally requiring
human intelligence, such as visual perception,
speech recognition, decision-making, and
translation between languages.
59. ACA Writer: Automatic Feedback on Written Essays
Gibson, A., Aitken, A., Sándor, Á., Buckingham Shum, S., Tsingos-Lucas, C., & Knight, S. (2017). Reflective
writing analytics for actionable feedback.
60. ETS Writing Mentor: Automatic Feedback on Written Essays
Madnani, N., Burstein, J., Elliot, N., Klebanov, B. B., Napolitano, D., Andreyev, S., & Schwartz, M. (2018, August). Writing
mentor: Self-regulated writing feedback for struggling writers. In Proceedings of the 27th International Conference on
Computational Linguistics: System Demonstrations (pp. 113-117).
63. Automatic Oral Presentation Feedback System
Ochoa, X., Domínguez, F., Guamán, B., Maya, R., Falcones, G., & Castells, J. (2018, March). The rap system:
automatic feedback of oral presentation skills using multimodal analysis and low-cost sensors. In Proceedings
of the 8th international conference on learning analytics and knowledge (pp. 360-364). ACM.
77. Medical Collaboration Feedback
Martinez-Maldonado, R., Power, T., Hayes, C., Abdiprano, A., Vo, T., Axisa, C., & Buckingham Shum, S. (2017, March). Analytics meet
patient manikins: challenges in an authentic small-group healthcare simulation classroom. In Proceedings of the seventh
international learning analytics & knowledge conference (pp. 90-94). ACM.
78.
79. Multimodal Transcript
Ochoa, X. et al. Multimodal Transcript of Face-to-Face Group-Work Activity Around Interactive Tabletops
CrossMMLA Workshop, Learning Analytics and Knowledge conference 2018, In Print