Do You Remember What You Know? Towards an understanding of the cognitive processes involved in the testing effect

There is an old saying that you cannot fatten a hog by weighing it, which means that the simple act of weighing a pig every day will not increase its weight. This saying is sometimes employed by opponents of the increase of the use of tests in educational practice, because simply testing students on their knowledge will not make them any smarter. Although this is probably true, using tests to assess students’ knowledge level seems inevitable in educational practice and is not a bad thing per se. It can be used to indicate where a student stands against peers or a fixed standard after a learning phase, but it can also be used during a learning phase to guide student learning with help from feedback obtained by the results of a test. One of the propositions belonging to this dissertation therefore is: You cán fatten a pig by weighing it! This proposition is not stated to claim that students could become smarter by testing them frequently, but that students can benefit from taking tests. In particular, one insight from cognitive psychology strongly suggests that testing students on their knowledge can strengthen their memory for that knowledge. This insight is called the testing effect and is named after the empirical finding that testing students’ memory after a

Training self-regulated learning skills with video modeling examples: Do task-selection skills transfer?

Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and task-selection, is effective for improving adolescents’ problem-solving posttest performance after self-regulated learning. In these examples, the models used a specific task-selection algorithm in which perceived mental effort and self-assessed performance scores were combined to determine the complexity and support level of the next task, selected from a task database. In the present study we aimed to replicate prior findings and to investigate whether transfer of task-selection skills would be facilitated even more by a more general, heuristic task-selection training than the task-specific algorithm. Transfer of task-selection skills was assessed by having students select a new task in another domain for a fictitious peer student. Results showed that both heuristic and algorithmic training of self-assessment and task-selection skills improved problem-solving posttest performance after a self-regulated learning phase, as well as transfer of task-selection skills. Heurist

Training self-regulated learning skills with video modeling examples: Do task-selection skills transfer?

Self-assessment and task-selection skills are crucial in self-regulated learning situations in which students can choose their own tasks. Prior research suggested that training with video modeling examples, in which another person (the model) demonstrates and explains the cyclical process of problem-solving task performance, self-assessment, and task-selection, is effective for improving adolescents’ problem-solving posttest performance after self-regulated learning. In these examples, the models used a specific taskselection algorithm in which perceived mental effort and self-assessed performance scores were combined to determine the complexity and support level of the next task, selected from a task database. In the present study we aimed to replicate prior findings and to investigate whether transfer of task-selection skills would be facilitated even more by a more general, heuristic task-selection training than the task-specific algorithm. Transfer of task-selection skills was assessed by having students select a new task in another domain for a fictitious peer student. Results showed that both heuristic and algorithmic training of self-assessment and task-selection skills improved problem-solving posttest performance after a self-regulated learning phase, as well as transfer of task-selection skills. Heuristic training was not more effective for transfer than algorithmic training. These findings show that example-based self-assessment and task-selection training can be an effective and relatively easy to implement method for improving students’ self-regulated learning outcomes. Importantly, our data suggest that the effect on task-selection skills may transfer beyond the trained tasks, although future research should establish whether this also applies when trained students perform novel tasks themselves

Effects of study intention and generating multiple choice questions on expository text retention

Teachers often recommend their students to generate test questions and answers as a means of preparing for an exam. There is a paucity of research on the effects of this instructional strategy. Two recent studies showed positive effects of generating test questions relative to restudy, but these studies did not control for time on task. Moreover, the scarce research available has been limited to the effects of generating open-ended questions. Therefore, the aim of this study was to investigate whether generating multiple-choice test questions would foster retention (as measured by a multiple-choice test) relative to restudy when time would be kept constant across conditions. Using a 2 × 2 design, university students (N = 143) studied a text with the intention of either generating test items or performing well on a test, and then either generated multiple-choice items or restudied the text. Retention was measured by means of a multiple-choice test, both immediately after learning and after a one-week delay. Results showed no effects of study intention. Generating multiple-choice items resulted in lower test performance than restudying the text for the same amount of time