Just Enough Practice

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Just Enough Practice
Just enough practice.png
Contributors
Last modification July 8, 2015
Source {{{source}}}
Pattern formats OPR Alexandrian
Usability
Learning domain General
Stakeholders Students, Teachers, System developers
Production
Data analysis Student affect and interaction behavior in ASSISTments
Confidence
Evaluation PLoP 2015 writing workshop, Talk:ASSISTments
Application ASSISTments
Applied evaluation ASSISTments

If academic risk takers become frustrated when they are asked to repeatedly answer problems that test skills they have already mastered, then change the problem type and/or topic after students master it.

Context

Students are asked to practice a particular skill through exercises in an online learning system. Teachers design the problems for the exercise in the online learning system. They also provide corresponding answers and feedback for each problem, and design their presentation sequence. Problems may vary in type (e.g., multiple choice, true or false), topic (e.g., addition, subtraction), and difficulty.

Problem

Academic risk takers become frustrated when they are asked to repeatedly answer problems that test skills they have already mastered.

Forces

  1. Practice. Students need practice to learn a skill[1][2][3]. It leads to greater improvements in performance during early sessions, but additional practice sessions lead to smaller improvement gains over time[4].
  2. Expertise reversal. Presenting students information they already know can impose extraneous cognitive load and interfere with additional learning[5].
  3. Risk taking. Academic risk takers are students who prefer challenging tasks because they want to maximize learning and feedback[6][7][8]. They are often intrinsically motivated, explore concepts they do not understand, and can cope with negative emotions resulting from failure[9].
  4. Limited resources. Student attention and patience is a limited resource possibly affected by pending deadlines, upcoming tests, achievement in previous learning experiences, motivation, personal interest, quality of instruction, and others[10][11].

Solution

Therefore, change the problem type and/or topic after students master it.

Student mastery can be assessed in different ways such as, counting the number of times a student correctly answered a problem type and/or topic, or using individualized statistical models for predicting student knowledge[12].

Consequences

Benefits

  1. Students get enough practice to learn the skill, but not too much to over-practice it.
  2. Students do not spend unnecessary time practicing skills they already mastered.
  3. Students practice on problems that challenge them.
  4. Students with better learning experiences are more inclined to continue learning.

Liabilities

  1. If skill mastery is incorrectly predicted, the system can still cause over-practice on a skill or worse, prevent students from practicing a skill enough before it is mastered.

Evidence

Literature

Cen, Koedinger and Junker (2007[13]) used data mining approaches to show that students had similar learning gains when they over-practiced a skill and stopped practicing a skill after mastery. However, it took less time when students stopped practicing after mastery. Instead of over-practicing, they suggested that students switch to learning other skills.

Discussion

In a meeting with Ryan Baker and his team at Teacher's College in Columbia University, Neil Heffernan and his team at Worcester Polytechnic Institute, and Peter Scupelli and his team at the School of Design in Carnegie Mellon University (i.e., ASSISTments stakeholders), the team agreed that too much repetition can be problematic and controlling the number of times a problem type is presented to students can address the problem.

David West, the pattern's shepherd at PLoP 2015, also considered the pattern definition acceptable.

Data

According to ASSISTments math online learning system data, frustration correlated with students repeatedly answering problems they have mastered.

Related patterns

The pattern can be used in conjunction with Spiral to help students master a subset of the larger topic through practice, before moving on to the next subtopic.

Example

A teacher designs homework with different types of math problems (e.g., decimal addition, subtraction, multiplication, and division). He/she can use the online learning system’s control mechanism to switch between problem types whenever a student shows mastery on a particular type. The number of times a student answered each problem type correctly can be used to identify mastery. For example, if the student correctly answers 3 decimal-addition problems, then the student will be asked to advance to decimal-subtraction problems. Otherwise, the student will continue answering decimal-addition problems.

References

  1. ↑ Clark, R. C., and Mayer, R. E. (2011). E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning. John Wiley & Sons.
  2. ↑ Sloboda, J. A., Davidson, J. W., Howe, M. J., and Moore, D. G. (1996). The role of practice in the development of performing musicians. British journal of psychology, 87(2), 287-310.Sweller, J., & Cooper, G. A. 1985. The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2(1), 59--89.
  3. ↑ Tuffiash, M., Roring, R. W., and Ericsson, K. A. (2007). Expert performance in SCRABBLE: implications for the study of the structure and acquisition of complex skills. Journal of Experimental Psychology: Applied, 13(3), 124.
  4. ↑ Rohrer, D. and Taylor, K. (2006). The effects of over-learning and distributed practice on the retention of mathematics knowledge. Applied Cognitive Psychology, 20, 1209--1224.
  5. ↑ Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional science, 32(1-2), 9-31.
  6. ↑ Clifford, M. M. (1988). Failure tolerance and academic risk‐taking in ten‐to twelve‐year‐old students. British Journal of Educational Psychology, 58(1), 15-27.
  7. ↑ Clifford, M. M. (1991). Risk taking: Theoretical, empirical, and educational considerations. Educational Psychologist, 26(3-4), 263-297.
  8. ↑ Meyer, D. K., and Turner, J. C. (2002). Discovering emotion in classroom motivation research. Educational psychologist, 37(2), 107-114.
  9. ↑ Boekaerts, M. (1993). Being concerned with well-being and with learning. Educational Psychologist, 28(2), 149-167.
  10. ↑ Arnold, A., Scheines, R., Beck, J. E., and Jerome, B. (2005). Time and attention: Students, sessions, and tasks. In Proceedings of the AAAI 2005 Workshop Educational Data Mining (pp. 62-66).
  11. ↑ Bloom, B. S. (1974). Time and learning. American psychologist, 29(9), 682.
  12. ↑ Yudelson, M. V., Koedinger, K. R., and Gordon, G. J. (2013). Individualized bayesian knowledge tracing models. In Artificial Intelligence in Education (pp. 171-180). Springer Berlin Heidelberg.
  13. ↑ Cen, H., Koedinger, K. R., and Junker, B. 2007. Is Over Practice Necessary?-Improving Learning Efficiency with the Cognitive Tutor through Educational Data Mining. Frontiers in Artificial Intelligence and Applications, 158, 511.
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