Professor Emeritus: Theodore Frick
Research Groups, Fall Semester 2014
Although now retired, I continue to mentor research and development projects.
I am currently investigating effectiveness of instructional principles. To do this, my research groups will analyze patterns of learning activities associated with those principles which lead to student achievement in online education. In other words, how well do those instructional principles work when they are used with students who are trying to learn something?
The main research question we are addressing:
To the extent students experience First Principles of Instruction, what is the likelihood of student learning achievement?
- If fewer First Principles are experienced, is the likelihood of student learning achievement lower?
- If more First Principles are experienced, is the likelihood of student learning achievement higher?
First Principles of Instruction include:
- Provision of authentic tasks or problems, sequenced from simple to complex.
- Activation (helping students connect what they already know with what is to be newly learned).
- Demonstration (of what is to be learned).
- Application (where students try to do the tasks or solve problems with instructor guidance and feedback).
- Integration (of what is learned into students' own lives).
See Merrill (2012), First Principles of Instruction, for further information.
Two studies are planned over the next 2+ years:
In our first study, we will investigate the effectiveness First Principles embodied in the Plagiarism Tutorial. This online learning resource is used by literally hundreds of thousands of people every year.
In our second study, we plan to investigate patterns of learning within the Diffusion Simulation Game. The DSG provides players with experience in applying strategies to help people adopt an innovation (based on Everett Rogers work on diffusion and adoption of innovations). A beta version of a newer DSG is also available, as well as an abridged version.
What is MAPSAT?
MAPSAT differs from traditional quantitative educational research methods, where variables are measured separately and then relations among variables are analyzed statistically. In MAPSAT, relations themselves are empirically observed and coded.
In APT, measures of relations are determined by relative frequency and/or duration of occurrences of observed temporal patterns. In other words, researchers code sequences of occurrences of events using defined categories from multiple classifications in an observation system. This results in a temporal map for each unique observed entity (e.g., each student who tries to learn via our Plagiarism Tutorial).
Each temporal map in APT can be represented by a spreadsheet. The rows in the spreadsheet represent successive moments in time; the columns represent the classifications in the observation system; and category names are entered by an observer into spreadsheet cells. The entries into the cells represent the temporal order of specific empirical events which are observed to occur within each classification column. The rows in the spreadsheet are labled by the date and time of each event occurrence. After observations are completed, a researcher subsequently can count specific qualitative patterns within each unique temporal map, as well as sum the durations of a particular temporal pattern.
For example, in the studies of First Principles of Instruction, we will be counting patterns which represent the sequence of specific instructional principles which are followed by student mastery of the learning objectives. There will be a temporal map for each student who goes through the online tutorial or plays the online game. Observations of event occurrences will be done by computer software embedded in the online instruction which will be using specific codes that the researchers have previously associated with each activity (e.g., this activity is an instance of the Application principle, or that activity is an instance of the Activation principle). When a student takes a test, computer software will classify the student as a master or nonmaster of the learning objectives. This will result in literally thousands of temporal maps. Probabilities of event sequences leading to student mastery can be estimated by APT software which we are also developing. This software will scan the temporal maps for occurrences of temporal patterns and count them.
Analysis of Patterns in Configurations (APC) is the other MAPSAT method. In APC, measures of 17 different properties of structural configurations are determined, including interdependence, wholeness, integration, hierarchical order and complexity. For further information on APC, see ATIS Graph Theory (Thompson, 2008). Structural properties are represented by a digraph of specific affect relations. A digraph consists of vertices (points) and edges (lines connecting points).
The most salient difference: MAPSAT measures relations, whereas quantitative statistical methods relate measures. This is not a play on words, rather a profound difference in approach to measurement and analysis in empirical research studies.
MAPSAT measures can be subsequently analyzed with traditional statistical methods. Measures of relations can be treated in aggregate through means (averages), standard deviations, probability estimates, confidence intervals, etc.
For illustrative examples of MAPSAT, see a short overview. For further examples and explanation, listen to a PowerPoint presentation on MAPSAT (about 50 minutes long; requires Flash plug-in for your Web browser).
Participation in a research group:
If you are an Indiana University student who is interested in joining me and contributing to this development and research, please contact me.
Past Research: A Sample
To get an idea of what students in my research groups and I have previously accomplished, see the reports below. Some links below to copyrighted reports require authentication with your IU username and password; other links do not.
SimEd: Developing and Studying Simulations and Games for Learning
Enfield, J., Myers, R., Lara, M., & Frick, T. (2012). Innovation diffusion: Assessment of strategies within the DIFFUSION SIMULATION GAME. Journal of Simulation and Gaming, 43(2) 188–214.
Kwon, S. & Frick, T. (2014). Design theory for instructional overlays within complex simulation games. Under review, Educational Technology Research and Development.
Kwon, S., Lara, M., Enfield, J. & Frick, T. (2013). Design and evaluation of a prompting instrument to support learning within the Diffusion Simulation Game. Journal of Educational Technology Systems, 41(3), 231-253.
Lara, M. (2013). Personality traits and performance in online game-based learning: Collaborative vs. individual settings. Bloomington, IN: Doctoral dissertation.
Lara, M., Myers, R., Frick, T., Aslan, S., & Michaelidou, T. (2010). A design case: Developing an enhanced version of the diffusion simulation game. International Journal of Designs for Learning, 1(1). IJDL online.
Myers, R. (2012). Analyzing interaction patterns to verify a simulation/game model. Bloomington, IN: Doctoral dissertation.
Myers, R. & Frick, T. (2014). Using pattern matching to assess game play. Under review as a chapter in Serious games analytics.
MAPSAT: Map & Analyze Patterns & Structures Across Time
Frick, T., Howard, C., Barrett, A., Enfield, J., & Myers, R. (2009). Alternative research methods: MAPSAT your data to prevent aggregation aggravation. Paper presented at the annual conference of the Association for Educational Communications & Technology, Louisville, KY.
Frick, T., Myers, R., Thompson, K. & York, S. (2008). New ways to measure systemic change: Map & Analyze Patterns & Structures Across Time (MAPSAT). Featured research paper presented at the annual conference of the Association for Educational Communications & Technology, Orlando, FL.
Howard C. D., Barrett A. F., and Frick, T. W. (2010). Anonymity to Promote Peer Feedback: Pre-Service Teachers' Comments in Asynchronous Computer-Mediated Communication. Journal of Educational Computing Research, 43(1), 89-112.
Koh, J., & Frick, T. (2009). Instructor and student classroom interactions during technology skills instruction for facilitating preservice teachers’ computer self-efficacy. Journal of Educational Computing Research, 40(2), 207-224.
Frick, T. (1990). Analysis of Patterns in Time (APT): A Method of Recording and Quantifying Temporal Relations in Education. American Educational Research Journal, 27(1), 180-204.
Frick, T. (1992). Computerized Adaptive Mastery Tests as Expert Systems. Journal of Educational Computing Research, 8(2), 187-213.
IDCL: Instructional Design for Complex Learning
Enfield, J. (2012). Designing an educational game with Ten Steps to Complex Learning. Bloomington, IN: Doctoral dissertation.
Frick, T., Chadha, R., Watson, C., Wang, Y. & Green, P. (2009). College student perceptions of teaching and learning quality. Educational Technology Research and Development, 57(5), 705-720
Frick, T., Chadha, R., Watson & Zlatkovska, E. (2010). Improving Course Evaluations to Improve Instruction and Complex Learning in Higher Education. Educational Technology Research and Development, 58(2), 115-136.
Frick, T. (2014). The theory of totally integrated education: TIE. A monograph in five parts. Bloomington, IN.