Report: LEADS Meeting
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Report: LEADS Meeting 02 May'13, San Francisco Systematic Evaluation of the Effectiveness of TREs through Software Platform Development for Data Mining across Multiple Disciplines and Tracking Changes in Affective and Cognitive Growths . M. Sazzad Hussain

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Report: LEADS Meeting 02 May'13, San Francisco

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Report leads meeting 02 may 13 san francisco

Report: LEADS Meeting

02 May'13, San Francisco

Systematic Evaluation of the Effectiveness of TREs through Software Platform Development for Data Mining across Multiple Disciplines and Tracking Changes in Affective and Cognitive Growths

M. Sazzad Hussain

Software Engineering and Latte Research Group

The University of Sydney, Australia

([email protected])

Dr. Rafael A. Calvo (in absentia)


What do we want to measure

> Applications

- Detect learner affective states [1].

- Measure "engagement".

- Evaluate different interventions [2].

- Impact of tutor feedback [3].

- Measure working memory load [4].

- Measure learning gain [5].

What do we want to measure?

> Affect, Cognition and Behavior

- during interaction (e.g. ITS)


Two contexts to integrate with software platform tracer

Two Contexts to Integrate with SoftwarePlatform - Tracer

- BioWorld

“a problem-based environment where students solve clinical cases and receive expert feedback.”[6]

- Metatutor

“an adaptive hypermedia learning environment that is designed to detect, model, trace, and foster students’ self-regulated learning about human body systems.”[7]


What have we done so far

What have we done so far...

  • Started on March 1st 2013

  • FTP accounts and files shared by research groups.

    • Sample data (EyeTracker, FaceReader, Affectiva, MetaTutor Logs) and MetaTutor interaction screen video from Dr. Roger Azevedo's group.

    • Sample BioWorld interaction screen videos from Dr. Susanne P. Lajoie's group.

  • Tutoring Systems.

    • Successfully installed MetaTutor and BioWorld.

    • Exploring the systems for Tracer integration.

  • Python based parser program shared for MetaTutor

    • Tracer integration

    • We plan to design a parser for GSR signal for Tracer.


What have we done so far1

What have we done so far...

  • Tracer Planning and Development (overview)

Process MetaTutor Log

Process BioWorld Log

Process Video Features

Plugin

Data Mining

(e.g. Weka)

Other Libraries

/Plugins

Visualizations (e.g. sync data, analysis)

Database (e.g. config, file location)

TRACER

(Acquiring Data)

Storage (e.g videos, ITS logs)

Annotation

Import/Export (offline data)

Other Functionalities

Config File related to Experiment (INPUT LIST)


Proposed visualization

Proposed Visualization...

  • Tracer Planning and Development

    • Sample visualization (raw data)


Proposed visualization1

performance/efficacy/efficiency

(logs, predictor)

Proposed Visualization...

nonverbal feature

(e.g. gsr, temp, movement)

statistical/

machine learning

  • Tracer Planning and Development

    • Sample visualization (data mining)

Rating / Value

Self-Reports

(affect, confidence)

Hypothesis/ Order test

time

Setting subgoal/Evidence

Learning Env.

planning, read, monitoring, evaluating, executing


Thank you slides http goo gl 5jvuh

Thank You!

Slides: http://goo.gl/5JvUh

  • M. Sazzad Hussain, O. AlZoubi, Rafael A. Calvo, S. D’Mello. (2011). Affect Detection from Multichannel Physiology during Learning Sessions with AutoTutor, The 15th International Conference on Artificial Intelligence in Education (AIED), Auckland, New Zealand,

  • R.A. Calvo, A. Aditomo, V. Southavilay and K. Yacef. (2012). The use of text and process mining techniques to study the impact of feedback on students’ writing processes. International Conference on the Learning Sciences.

  • P. A. Pour, M. Sazzad Hussain, O. AlZoubi, S. D’Mello, R. A. Calvo. (2010). The Impact of System Feedback on Learners, Affective and Physiological States, Proceedings of The 10th International Conference on Intelligent Tutoring Systems, pp 264-273, Pittsburgh, USA

  • M. Sazzad Hussain, R. A. Calvo, F. Chen., Automatic Cognitive Load Detection from Face. (to appear). Physiology, Task Performance and Fusion during Affective Interference. Interacting with Computers, Oxford Journals

  • Sidney D’Mello et. al (2010), A Time For Emoting: When Affect-Sensitivity Is and Isn’t Effective at Promoting Deep Learning, Intelligent Tutoring System, pp 245-254

  • Lajoie S. et.al. “Technology Rich Tools to Support Self-Regulated Learning and Performance in Medicine” International Handbook of Metacognition and Learning Technologies

  • Azevedo, R Rus, V., Cai, Z., & Lintean, M. (2008). MetaTutor: An adaptive hypermedia system for training and fostering self-regulated learning about complex science topics. Paper presented at annual meeting of the Society for Computers in Psychology, Chicago, IL


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