problem solving & educational technology lab

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The PSET lab investigates many areas of learning and technology, organized under three categories:
    Problem Solving
    Educational Technology
    Human Computer Interaction

Problem Solving

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Practice Schedules

We are investigating the effect of practice schedules (e.g., blocked, random) on skill acquisition, retention, and transfer in cognitive and motor skills. Our work focuses on identifying cognitive processes responsible for the effect of practice schedules on learning, and documenting how amount of practice can change these effects.

Publications (selected):

Gane, B. D. & Catrambone, R. (2011). Extended practice in motor learning under varied practice schedules: Effects of blocked, blocked-repeated, and random schedules. In Proceedings of the Human Factors and Ergonomics Society 55th Annual Meeting (pp. 2143- 2147). Santa Monica, CA: Human Factors and Ergonomics Society.

Gane, B. D., & Catrambone, R. (2009, April). Practice schedules in cognitive skill acquisition: Effects of example order on categorization and problem solving. Paper presented at the American Educational Research Association annual meeting, San Francisco, CA (AERA '09).

Gane, B. D., & Catrambone, R. (2007). Ordering worked examples to promote categorization. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society (pp. 1019-1024). Austin, TX: Cognitive Science Society.

Instructions for Procedural Tasks

Our studies in this area have focused on how people learn to do procedural tasks and how these skills are transferred to new situations. Of particular interest is how people use instructions to help them understand and learn what to do, and how instructions can be structured and designed with task and context in mind to increase efficiency of performance and learning.

Publications (selected):

Eiriksdottir, E. & Catrambone, R. (2007). Using instructions in procedural tasks. In D. S. McNamara & J. G. Trafton (Eds.), Proceedings of the 29th Annual Cognitive Science Society (pp. 959-964). Austin, TX: Cognitive Science Society.

Eiriksdottir, E., & Catrambone, R. (2008). How do people use instructions in procedural tasks. Proceedings of the Human Factors and Ergonomics Society 52nd Annual Meeting, 673-677.

Subgoal Learning

Students often learn a series of steps for solving problems when they study worked examples. These steps typically do not have a lot of organization or meaning and therefore they are "brittle", that is, they can not be easily adapted for new problems that are not just like examples students have studied. If examples can be revised so that they emphasize subgoals, which represent the "purpose" for sets of steps, then learners are more successful solving novel problems. Problems within a section of a domain tend to share the same subgoals, therefore students who have learned a subgoal structure are more likely to be able to use the subgoals to help them adapt old procedures.

Publications (selected):

Catrambone, R. (1996). Generalizing solution procedures learned from examples. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22(4), 1020-1031.

Catrambone, R. (1995). Aiding subgoal learning: Effects on transfer. Journal of Educational Psychology, 87(1), 5-17.

Task Analysis

One common theme in our research is the use of task analysis to identify the knowledge that learners need to know in order to solve problems or carry out a task. We have developed a task analysis procedure in which domain expert(s) solve problems while being questioned about the "how" and "why" of their solutions; this information yields declarative and procedural knowledge that can be used to design instructional materials.
Our research on task analysis involves two tracts: (1) using the task analysis to identify prerequisite knowledge in order to create learning materials for experiments, and (2) standardizing the task analysis method in order to allow other researchers to use the method themselves.

Publications (selected):

Gerjets, P., Scheiter, K., & Catrambone, R. (2004). Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional Science, 32, 33-58.

Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127(4), 355-376.

Active and Constructive Studying

We are currently investigated an educational framework proposed by Chi (2009) that links overt study activities with outcomes via the underlying cognitive processes experienced by learners. Learning is classified along a continuum of passive, active, and constructive. The framework posits that constructive activities yield the best learning, passive yield the poorest, and active is somewhere in between. We are addressing a variety of issues: comparison of all three levels in a single experiment, multiple measures to ensure student compliance with the study activities, assessment of student learning ranging from factual recall to inference making, investigation of retention, and consideration of time-on-task while learning.

Publications (selected):

(Research currently in progress)

Educational Technology

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Learning from Multimedia Instructions

Prior research has demonstrated that multimedia instructions, which present information in more than one format, benefit individuals with high spatial ability more than those with low spatial ability. One of our research goals is to determine which aspects of spatial ability are critical, and to determine whether the instructions can be designed to benefit those with low spatial ability.

Publications (selected):

Chen, D. & Catrambone, R. (2014). Effects of multimedia interactivity on spatial task learning outcomes. Proceedings of the 58th Annual Meeting of the Human Factors and Ergonomics Society (pp. 1356-1360). Santa Monica, CA: Human Factors and Ergonomics Society. (PDF)

Kline, K.A., Catrambone, R. (2009). The influence of spatial ability on multimedia learning. Proceedings of the Human Factors and Ergonomics Society 54th Annual Meeting. San Antonio, TX.

Zolna, J. S. and Catrambone, R. (2005). Understanding the locus of modality effects and how to effectively design multimedia instructional materials. Proceedings of the 12th International Conference on Artificial Intelligence in Education, Amsterdam, Netherlands.

Zolna, J.S. and Catrambone, R. (2003). Semantic modality in information design: improving learning efficiency in multimedia environments. Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting, Denver, CO.

Mobile Learning

Mobile information and communications technologies (ICT), such as mobile phones, have the potential to support learning in new ways. We have investigated statistics learning through traditional paper-and-pencil methods and through text messages, both in lab settings and in authentic contexts. Our initial results suggest that the mobile learning application is easy to use and understand and can replaced the need for calculators and timers found in the paper-and-pencil condition.

Publications (selected):

Bujak, K. R. & Catrambone, R. (2008, November). Using Text Messages to Support Complex Learning Tasks. Poster presented at the Psychonomic Society 49th Annual Meeting, Chicago, IL, USA. (PDF)

Algorithm Animation

Computer science instructors sometimes use animated examples of algorithms operating on data structures. We have studied whether these animations can be more effective than static diagrams, and what features of animations might increase their pedagogical value. Similar to learning from diagrams or text, animations seem most effective when learners are cognitively engaged; such as by making predictions about the algorithm's behavior or answering questions while watching the animations improves learning.

Publications (selected):

Gane, B. D., & Catrambone, R. (2006). Give learners questions to answer while they watch animated examples. In S. A. Barab, K. E. Hay, & D. T. Hickey (Eds.), Proceedings of the Seventh International Conference on Learning Sciences - ICLS '06 (pp. 922-923). Mahwah, NJ: Erlbaum.

Catrambone, R., & Seay, A.F. (2002). Using animation to help students learn computer algorithms. Human Factors, 44(3), 495-511.

Byrne, M.D., Catrambone, R., & Stasko, J.T. (1999). Evaluating animations as student aids in learning computer algorithms. Computers & Education, 33, 253-278.

Human Computer Interaction

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Virtual Agents and Social Facilitation

Our studies in this area have investigated whether virtual humans can evoke a social facilitation response. Participants performed easy and hard tasks alone, in the company of another person, or in the company of a virtual human on a computer screen. As with a human, virtual humans can produce social facilitation. The results suggest that designers of virtual humans should be mindful about the social nature of virtual humans; a design decision as to when and how to present a virtual human should be a deliberate and informed decision. An ever-present virtual human might make learning and performance difficult for challenging tasks.

Publications (selected):

Park, S., & Catrambone, R. (2007). Social Facilitation Effects of Virtual Humans. Human Factors: The Journal of the Human Factors and Ergonomics Society, 49, 1054-1060.

Photo of various journals on shelf