The effects of worked examples presentation on sub-cognitive loads

One promising technique for helping students in fulfilling complex problem solving tasks is through learning with worked example. Although worked example approach is the most prominent technique discussed in the literature of cognitive load theory, there is still very little scientific understanding of managing the students’ sub-cognitive load, namely intrinsic, extraneous and germane load on different complexity of worked example approach especially in engineering domain. Thus, this study was conducted to investigate the effects worked examples presentation on sub-cognitive loads among electrical engineering students. In this research, the worked examples were presented in three different sequences: (i) all examples were of same level of difficulty (ii) from difficult to easy example; (iii) from easy to difficult example. Data were collected from 82 students (Condition (i) =27; Condition (ii) = 34; condition (iii) = 21) of Diploma in Electrical Engineering Program at three selected polytechnics. The inventory tool of sub cognitive load were given to students after each teaching and learning session ends. The findings suggested that worked examples presented in easy to difficult format could be one of the approaches that is more efficient to manage students’ cognitive load and effective to be applied in engineering lesson

Best practice, best teaching

Keynote address discussing examples from my own, colleagues, and attendees practice. Conference participants worked in groups to share and build upon their existing teaching and learning strategies

Interactive Worked Examples Learning Strategy in the Assistment.com System

A study comparing the learning support facilitated by interactive worked examples and scaffolding questions was made using the Assistments.org System. The problems used were chosen from questions from past examinations in the 8th grade Mathematics MCAS. After analyzing the data it was conclusive that learning occurred by using either interactive worked examples or scaffolding questions. It was inconclusive, however, that there is a difference between the two learning methods

Learning to solve ill-defined statistics problems: does self-explanation quality mediate the worked example effect?

Extensive research has established that successful learning from an example is conditional on an important learning activity: self-explanation. Moreover, a model for learning from examples suggests that self-explanation quality mediates effects of examples on learning outcomes (Atkinson et al. in Rev Educ Res 70:181–214, 2000). We investigated self-explanation quality as mediator in a worked examples—problem-solving paradigm. We developed a coding scheme to assess self-explanation quality in the context of ill-defined statistics problems and analyzed self-explanation data of a study by Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016). Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016) investigated whether the worked example effect depends on prior knowledge, working memory capacity, shifting ability, and fluid intelligence. In our study, we included these variables to jointly explore mediating and moderating factors when individuals learn with worked examples versus through problem-solving. Seventy-four university students (mean age = 23.83, SD = 5.78) completed an open item pretest, self-explained while either studying worked examples or solving problems, and then completed a post-test. We used conditional process analysis to test whether the effect of worked examples on learning gains is mediated by self-explanation quality and whether any effect in the mediation model depends on the suggested moderators. We reproduced the interaction effects reported by Schwaighofer et al. (J Educ Psychol 108: 982–1000, 2016) but did not detect a mediation effect. This might indicate that worked examples are directly effective because they convey a solution strategy, which might be particularly important when learning to solve problems that have no algorithmic solution procedure

Individual differences in the preference for worked examples: lessons from an application of dispositional learning analytics

Worked-examples have been established as an effective instructional format in problem-solving practices. However, less is known about variations in the use of worked examples across individuals at different stages in their learning process in student-centred learning contexts. This study investigates different profiles of students’ learning behaviours based on clustering learning dispositions, prior knowledge, and the choice of feedback strategies in a naturalistic setting. The study was conducted on 1,072 students over an eight-week long introductory mathematics course in a blended instructional format. While practising exercises in a digital learning environment, students can opt for tutored problem-solving, untutored problem-solving, or call worked examples. The results indicated six distinct profiles of learners regarding their feedback preferences in different learning phases. Finally, we investigated antecedents and consequences of these profiles and investigated the adequacy of used feedback strategies concerning ‘help-abuse’. This research indicates that the use of instructional scaffolds as worked-examples or hints and the efficiency of that use differs from student to student, making the attempt to find patterns at an overall level a hazardous endeavour

Problem solving in basic physics: Effective self-explanations based on four elements with support from retrieval practice

Self-explanation, a learning strategy where students explain to themselves the steps taken in a worked example, is an effective learning strategy in early cognitive skill acquisition. However, many physics students produce self-explanations of low quality. There is also a lack of guidelines for what students should seek to explain when studying worked examples. Therefore, the overarching purpose of this article is to investigate how we can improve students’ self-explanations of worked examples. We pursue the following two general research questions: (1) What knowledge representations should students seek while self-explaining worked examples to maximize their learning? (2) Can retrieval practice of physics principles and their conditions of application potentiate students’ learning from self-explaining worked examples? In two studies ( n = 1 8 and N = 5 4 ), we qualitatively categorized and quantified the students’ written self-explanations. Our results indicate that to produce useful knowledge, self-explanations of the physics model in worked examples should explain what principle is used, how the principle is set up, and how the conditions of application are met for the principle, while explanations of the mathematical procedures should contain action descriptions, goals, and conditions ( r = 0.30 –0.50). Through a quasiexperimental ( N = 5 7 ) and an experimental ( N = 5 4 ) test, we found evidence that retrieval practice of physics principles and their conditions of application before self-explanation can have a medium-sized effect on post-test problem-solving scores and that it can increase the quality of students’ self-explanations. Using retrieval practice to potentiate learning from more complex learning strategies is a novel and promising approach to improve physics students’ learning.publishedVersio

Effects Of The Use Of Calculators, The Polya Heuristic and Worked Examples On Performance in Learning Mathematics From a Cognitive Load Perspective

Cognitive load theory holds that if an instructional format reduces extraneous cognitive load andlor increases germane cognitive load during learning, as compared to another instructional format, then it will be more efficient in promoting learning, provided that the total cognitive load does not exceed the total mental resources. Based on this premise, a series of four experiments was conducted to investigate the use of calculators, the Polya heuristic and worked examples in an attempt to generate an alternative instructional format that is more instructionally efficient than the conventional one to teach Percentage to Form 1 students. It was hypothesized that the use of calculators and the use of worked examples can reduce cognitive load during leaming and lead to better learning performance while the use of the Polya heuristic increases cognitive load and reduces learning performance. The results from the experiments indicated that the use of calculators and worked examples reduced cognitive load and led to better learning performance but the use of the Polya heuristic produced zero effects. Based on the results from the experiments, an alternative instructional format, called the calcworked instructional format, that incorporated the use of calculators and the use of partial completion worked examples was generated. The calcworked instructional format was compared with the conventional instructional format and was found to be more instructionally efficient

Examples and tutored problems: How can self-explanation make a difference to learning?

“The final publication is available at link.springer.com”.Learning from worked examples has been shown to be superior to unsupported problem solving in numerous studies. Examples reduce the cognitive load on the learner's working memory, thus helping the student to learn faster or deal with more complex questions. Only recently researchers started investigating the worked example effect in Intelligent Tutoring Systems (ITSs). We conducted a study to investigate the effect of using worked examples in combination with supported problem-solving in SQL-Tutor. We had three conditions: Examples Only (EO), Problems Only (PO), and Alternating Examples/Problems (AEP). After completing a problem, students received a self-explanation prompt that focused on the concepts used in the problem, to make sure that students acquire conceptual knowledge. On the other hand, examples were followed by self-explanation prompts that focused on procedural knowledge. The study showed that the AEP and PO conditions outperformed EO in learning gain, while AEP outperformed PO in conceptual knowledge acquisition. Therefore, interleaving examples with supported problems is an optimal choice compared to using examples or supported problems only in SQL-Tutor

Example-based learning: Integrating cognitive and social-cognitive research perspectives

Example-based learning has been studied from different perspectives. Cognitive research has mainly focused on worked examples, which typically provide students with a written worked-out didactical solution to a problem to study. Social-cognitive research has mostly focused on modeling examples, which provide students the opportunity to observe an adult or a peer model performing the task. The model can behave didactically or naturally, and the observation can take place face to face, on video, as a screen recording of the model's computer screen, or as an animation. This article reviews the contributions of the research on both types of example-based learning on questions such as why example-based learning is effective, for what kinds of tasks and learners it is effective, and how examples should be designed and delivered to students to optimize learning. This will show both the commonalities and the differences in research on example-based learning conducted from both perspectives and might inspire the identification of new research questions

Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice

Two complementary processes may be distinguished in learning a complex cognitive skill such as computer programming. First, automation offers task-specific procedures that may directly control programming behavior, second, schema acquisition offers cognitive structures that provide analogies in new problem situations. The goal of this paper is to explore what the nature of these processes can teach us for a more effective design of practice. The authors argue that conventional training strategies in elementary programming provide little guidance to the learner and offer little opportunities for mindful abstraction, which results in suboptimal automation and schema acquisition. Practice is considered to be most beneficial to learning outcomes and transfer under strict conditions, in particular, a heavy emphasis on the use of worked examples during practice and the assignment of programming tasks that demand mindful abstraction from these examples