Just Enough Practice

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Just Enough Practice
Contributors
Last modification June 17, 2015
Source {{{source}}}
Pattern formats OPR Alexandrian
Usability
Learning domain General
Stakeholders Students
Teachers
Confidence
Evaluation PLoP 2015 writing workshop
Talk:ASSISTments
Application ASSISTments
Applied evaluation ASSISTments

If students become frustrated when they are asked to repeatedly answer similar problems, then change the problem when students have mastered it.

Just enough practice

Context

An online learning system allows teachers to assign exercises (or homework) for students to practice a particular skill. Teachers design problems with corresponding answers, feedback, and determine the problem sequence. Problems vary in type (e.g., multiple choice, true or false), topic (e.g., addition, subtraction), and difficulty.

Evidence

Literature

Cen, Koedinger and Junker (2007)[1] used data mining to show that when students practice a skill enough to master it, they get similar learning gains and save more time compared to over-practicing the skill. Instead of over-practicing, they suggested that students should switch to learning other skills

Data

Data from the ASSISTments math online learning system also showed that students experienced frustration after answering the same type of problem repeatedly and getting it correct every time [1].

Forces

  1. Pedagogy. Students need practice to learn a skill.
  2. Practice benefits. Practice leads to greater improvements in performance during early sessions, but additional practice sessions lead to smaller improvement gains over time.
  3. Prior knowledge. A student’s prior knowledge explains why some questions are easier or harder than others.
  4. Affect. Student affect and cognitive state color students’ learning experience. For instance, they get frustrated when asked to solve the same problem type repeatedly.
  5. Limited resources. Student attention and patience is a limited resource. .

Problem

Students become frustrated when they master a skill and are asked to repeatedly answer similar problems.

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 [2].

Consequences

Benefits

  1. Students get enough practice to learn the skill, but not too much to over-practice it.
  2. Students get more practice on problems that are harder for them, but less on problems they find easier.
  3. Students solve problems that build on their prior knowledge and have time to learn new skills.
  4. Positive learning experiences can motivate students to engage with learning problems.
  5. Students spend less time and effort learning a skill.

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.

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 in solving a particular problem 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.

Related patterns

This 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.

References

  1. Cen, H., Koedinger, K. 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.
  2. 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.