Educational Research Methods 2014 - Revision history

Koedinger at 14:21, 24 October 2016

2016-10-24T14:21:41Z

← Older revision		Revision as of 14:21, 24 October 2016
Line 1:		Line 1:
			A new version of this course is [[Educational Research Methods 2017]].

	===Research Methods for the Learning Sciences 05-748===		===Research Methods for the Learning Sciences 05-748===
	Spring 2014 Syllabus Carnegie Mellon University		Spring 2014 Syllabus Carnegie Mellon University

Koedinger: /* Experimental Research Methods (Koedinger) */

2014-04-17T21:15:10Z

Experimental Research Methods (Koedinger)

← Older revision		Revision as of 21:15, 17 April 2014
Line 423:		Line 423:

	=====Experimental Research Methods (Koedinger)=====		=====Experimental Research Methods (Koedinger)=====
	*4-22		*4-22 Continuation of Causal Inference
			*4-24
	**Reading: Trochim Ch 7 and 9		**Reading: Trochim Ch 7 and 9
	**Do two posts on Blackboard.		**Do two posts on Blackboard.
	**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt\|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt\|True-Experiments.ppt]]		**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt\|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt\|True-Experiments.ppt]]
	*4-24 NO CLASS
	*4-29		*4-29
	**Reading: Trochim Ch 10		**Reading: Trochim Ch 10

Koedinger: /* Educational Data Mining -- Causal Inference from Data (Scheines) */

2014-04-07T14:51:47Z

Educational Data Mining -- Causal Inference from Data (Scheines)

← Older revision		Revision as of 14:51, 7 April 2014
Line 401:		Line 401:
	*4-8		*4-8
	**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods		**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
	go to: http://oli~~.web~~.cmu.edu/~~openlearning~~/		Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
	in the ~~left~~ tab, ~~go to~~ "Prior work~~...~~" ~~and then~~ "Empirical Research Methods"		Scroll down and click on the rightmost tab, "Prior work (5)"
	~~click~~ on ~~Peek In~~		Click on "Empirical Research Methods" and then on "[Enter Course]"
	~~complete Unit~~ 2		Click on "CMU users sign in here" to login with your CMU account
			or "Enter Without an Account"
			Complete "UNIT 2: Regression, Prediction and Causation"
			**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
			**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
			***a) Tutorial on causal learning (my tutorial on Tetrad)
			***b) Educational Research I (overview of causal discovery in educational research)
			***c) Educational Research II (Martina explaining the paper you are assigned)
			**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

	*4-10 NO CLASS - Spring Carnival		*4-10 NO CLASS - Spring Carnival

	*4-15		*4-15
	**Read Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc \| PDF]]		**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc \| PDF]]

	*4-17 Continue discussion of Causal Inference from Data & TETRAD

			*4-17
			**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf \| Rau, Scheines, Aleven, & Rummel (2013]]

	=====Experimental Research Methods (Koedinger)=====		=====Experimental Research Methods (Koedinger)=====

Koedinger: /* Flex day (Koedinger) */

2014-03-28T13:55:20Z

Flex day (Koedinger)

← Older revision		Revision as of 13:55, 28 March 2014
Line 390:		Line 390:

	===== Flex day (Koedinger) =====		===== Flex day (Koedinger) =====
	*4-3 To be used in case of rescheduling or for a student-driven topic.		*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
	***And/or for Review of Projects or Past Topics		** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
	**Option1. More on Educational Data Mining		***Option1. More on Educational Data Mining
			***Option2. Return to Design Research & Qualitative Methods (Koedinger)
	**Option2. Return to Design Research & Qualitative Methods (Koedinger)
	***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)		***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
	***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf\|PDF]]		***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf\|PDF]]

Koedinger: /* Educational Data Mining -- Learning Curve Analysis (Koedinger) */

2014-03-28T13:53:00Z

Educational Data Mining -- Learning Curve Analysis (Koedinger)

← Older revision		Revision as of 13:53, 28 March 2014
Line 353:		Line 353:
	**Read the following paper and make two posts as usual.		**Read the following paper and make two posts as usual.
	***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf\|KoedingerMcLaughlinStamperEDM12.pdf]]		***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf\|KoedingerMcLaughlinStamperEDM12.pdf]]
	**In-class activity: Start on one of the two exercises (A or B) below. ~~In either case, for next Tuesday by 9am, provide~~ a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard.		**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
	*4-1		*4-1
	**In-class: ~~We will review assignments~~.		**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
	**Read the following paper and make two posts as usual.		**Read the following paper and make two posts as usual.
	***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf\|AIED2007_EDM_Zhang_ld_transfer.pdf]]		***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf\|AIED2007_EDM_Zhang_ld_transfer.pdf]]

Koedinger: /* Grading */

2014-03-25T21:36:07Z

Grading

← Older revision		Revision as of 21:36, 25 March 2014
Line 64:		Line 64:
	** 30% Before-class preparation, including reading reports, and in-class participation		** 30% Before-class preparation, including reading reports, and in-class participation
	** 40% Assignments		** 40% Assignments
	* Project & final paper - Initial ideas due Feb 15, research question and ~~lit review~~ due March 30, Final paper due May 10.		* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
	** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.		** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
	:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.		:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.

Koedinger: /* Educational Data Mining -- Learning Curve Analysis (Koedinger) */

2014-03-20T21:46:40Z

Educational Data Mining -- Learning Curve Analysis (Koedinger)

← Older revision		Revision as of 21:46, 20 March 2014
Line 359:		Line 359:
	***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf\|AIED2007_EDM_Zhang_ld_transfer.pdf]]		***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf\|AIED2007_EDM_Zhang_ld_transfer.pdf]]
	***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]		***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
	***Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]		***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

	Do A or B:		Do A or B:

Koedinger: /* Educational Data Mining -- Learning Curve Analysis (Koedinger) */

2014-03-20T21:45:15Z

Educational Data Mining -- Learning Curve Analysis (Koedinger)

← Older revision		Revision as of 21:45, 20 March 2014
Line 345:		Line 345:
	**BRING YOUR LAPTOP FOR ALL THESE SESSIONS		**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
	**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).		**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
	**~~Before class start~~ on the assignment and ~~at least~~ complete the ~~DataShop registration~~		***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
	**Read the following paper and make two posts as usual.		**Read the following paper and make two posts as usual.
	***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf\| Stamper-Koedinger-AIED2011.pdf]]		***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf\| Stamper-Koedinger-AIED2011.pdf]]
	***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf \| RittterSchooler01.pdf]]		***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf \| RittterSchooler01.pdf]]
	**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-~~2013_(Geom_Area_Unit_Spring_2010)~~.doc \| Learning-curve-assignment-~~2013~~.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.		**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc \| Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
	*3-27		*3-27
	**Read the following paper and make two posts as usual.		**Read the following paper and make two posts as usual.

Koedinger: /* Educational Data Mining -- Learning Curve Analysis (Koedinger) */

2014-03-20T21:09:30Z

Educational Data Mining -- Learning Curve Analysis (Koedinger)

← Older revision		Revision as of 21:09, 20 March 2014
Line 343:		Line 343:
	=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====		=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
	*3-25		*3-25
	**~~Readings~~:		**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
			**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
			**Before class start on the assignment and at least complete the DataShop registration
			**Read the following paper and make two posts as usual.
	***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf\| Stamper-Koedinger-AIED2011.pdf]]		***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf\| Stamper-Koedinger-AIED2011.pdf]]
	***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf \| RittterSchooler01.pdf]]		***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf \| RittterSchooler01.pdf]]
	**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2013_(Geom_Area_Unit_Spring_2010).doc \| Learning-curve-assignment-2013.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. (~~See my emails, original and followup, for further directions~~ on ~~how to do this assignment~~.)		**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2013_(Geom_Area_Unit_Spring_2010).doc \| Learning-curve-assignment-2013.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
	*3-27		*3-27
	**Read the following paper and make two posts ~~on the general topic of this reading and the last, namely, using educational technology data~~ as ~~a basis for discovering improvements to cognitive models~~.		**Read the following paper and make two posts as usual.
	***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf\|KoedingerMcLaughlinStamperEDM12.pdf]]		***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf\|KoedingerMcLaughlinStamperEDM12.pdf]]
	**~~Also~~, ~~do some thinking about~~ a ~~semester project so we can discuss~~ (and ~~I can give feedback~~) on ~~your possible ideas for a project~~.		**In-class activity: Start on one of the two exercises (A or B) below. In either case, for next Tuesday by 9am, provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard.
	*4-1		*4-1
	**~~Please finish off one of the two exercises you started for last~~ class. See A or B further below. In either case, provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard.		**In-class: We will review assignments.
	**~~ALSO,~~ make ~~a post about your idea for a course final project~~. ~~What method might you apply to address what research question?~~		**Read the following paper and make two posts as usual.
	**~~No required reading assignment.~~		***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf\|AIED2007_EDM_Zhang_ld_transfer.pdf]]
	***Optional readings:		***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
	***'''Optional:''' Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf\|AIED2007_EDM_Zhang_ld_transfer.pdf]]
	***Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
	***Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]		***Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Koedinger: /* Cognitive Task Analysis (CTA) (Koedinger) */

2014-02-14T13:53:24Z

Cognitive Task Analysis (CTA) (Koedinger)

← Older revision		Revision as of 13:53, 14 February 2014
Line 221:		Line 221:
	**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf\|AlevenITS2004.pdf]]		**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf\|AlevenITS2004.pdf]]
	**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf\|Klahr&carver88.pdf]]		**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf\|Klahr&carver88.pdf]]
	***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) ~~how~~ do the authors represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?		***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
	*Other possible readings:		*Other possible readings:
	**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf\|Lovett01CandI.pdf]]		**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf\|Lovett01CandI.pdf]]