Self-explanation Activities Research Articles

Digital Worked Example: an Experiment on Strategies to Enhance Computer Programming Skills

Learning programming in an example-based learning condition is beneficial to learners with low prior knowledge. This study explored digital worked example (DWE) with an instructional explanation to promote completion strategy and faded worked example (FWE) using screencast technology to promote self-explanation activity. Digital worked example (DWE), integrated with instructional explanation, a complete program that written in C-IDE and saved as a C source file for learners to run and modify to view the output. Fading worked example (FWE) designed to support interactivity using screencast; displays the steps to solve programming problems and fades one or more steps to promote self-explanation effect. Foundation Engineering students enrolled in C programming course for the first time participated in this study. An experiment with a control group (used DWE, n=53) and an experimental group (used FWE, n=51) carried out over seven weeks. Results from this study show that the effect of screencast not significantly effective when transferring basic programming knowledge to gain program writing skills. The DWE with instructional explanation is suitable for novices, and the impact of fading techniques in a worked example (FWE) using screencast is significantly effective in the near transfer performance but not for far transfer performance. However, participant perceived learning experience using worked example with different strategies shows a higher preference for FWE than DWE. The outcome of this study makes room for future research to focus on cognitive overload principles when using screencast technology for a worked example.

The effects of comprehension-test expectancies on metacomprehension accuracy.

A set of four experiments assessed the effects of establishing a comprehension-test expectancy (in contrast to a memory-test expectancy) on relative metacomprehension accuracy. Typically readers show poor relative metacomprehension accuracy while learning from text (i.e., they are unable to discriminate topics they have understood well from topics they have understood poorly). In the first experiment, both readers who were given no test expectancy and those who were given a memory-test expectancy made judgments that were more predictive of performance on memory tests than inference tests. However, readers who were given a comprehension-test expectancy made judgments that were more predictive of inference-test performance. This effect was replicated and extended in two additional experiments that showed an effect of comprehension-test expectancy even when no example test items were provided, and when the expectancy was established only after reading. A fourth experiment showed that establishing a comprehension-test expectancy still had an effect on accuracy even when metacomprehension accuracy was already being improved via a self-explanation activity. The results show robust and reliable benefits to metacomprehension accuracy from a comprehension-test expectancy that serves as portable knowledge that learners can apply to monitoring future learning from text. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Fostering medical students' clinical reasoning by learning from errors in clinical case vignettes: effects and conditions of additional prompting procedures to foster self-explanations.

The present study aims at fostering undergraduate medical students' clinical reasoning by learning from errors. By fostering the acquisition of "negative knowledge" about typical cognitive errors in the medical reasoning process, we support learners in avoiding future erroneous decisions and actions in similar situations. Since learning from errors is based on self-explanation activities, we provided additional prompting procedures to foster the effectiveness of the error-based instructional approach. The extent of instructional support in a web-based learning environment with erroneous clinical case examples was varied in a one-factorial design with three groups by either presenting the cases as (a) unsupported worked examples or by providing the participants with (b) closed prompts in the form of multiple-choice tasks or (c) with open reflection prompts during the learning process. Despite significant learning progress in all conditions, neither prompting procedure improved the learning outcomes beyond the level of the unsupported worked example condition. In contrast to our hypotheses, the unsupported worked example condition was the most effective with respect to fostering clinical reasoning performance. The effects of the learning conditions on clinical reasoning performance was mediated by cognitive load, and moderated by the students' self-efficacy. Both prompting procedures increased extraneous cognitive load. For learners with low self-efficacy, the prompting procedures interfered with effective learning from errors. Although our error-based instructional approach substantially improved clinical reasoning, additional instructional measures intended to support error-based learning processes may overtax learners in an early phase of clinical expertise development and should therefore only be used in moderation.

Training Interventions to Foster Skill and Will of Argumentative Thinking

ABSTRACTArgumentative thinking requires not only the skill to apply argumentative strategies such as supporting theories with evidence but also the will to apply these strategies by considering argumentative thinking to be both reasonable and worthwhile. Focusing on direct instruction for the initial acquisition of both skill and will, we developed a new short-term computer-based training intervention. We tested its effects on learning processes and learning outcomes in an experimental study with 147 German high school students. Our intervention fostered facets of both skill (i.e. a declarative knowledge about argumentation) and will (i.e., epistemic orientation, intellectual values, and epistemic knowledge). We gained additional insights into learning mechanisms, such as the mediating effects of the learners' self-explanation activities and the advantage of addressing will before skill.

Effects of experiential-based videos in multi-disciplinary learning

<p>This study examined the use of authentic experiential-based videos in self-explanation activities on 32 polytechnic students' learning and motivation, using a mixed method quasi-experimental design. The control group analysed a set of six pre-recorded videos of a subject performing the standing broad jump (SBJ). The experimental group captured videos of two subjects performing six variations of the SBJ. They then proceeded to analyse those videos as with the control group. All students then attempted a worksheet designed to elicit various levels of principled understanding of the topic on "projectile motion". In addition, data on students' motivation to learn and learning outcomes were measured by using three subscales of the Motivational Strategies for Learning Questionnaire (MSLQ) ― task value, critical thinking, and self-efficacy ― both before and after the intervention. Data were also collected through online pre- and post-tests, classroom observations as well as reflection journals. No significant differences between the experimental and control groups in terms of the post-intervention results were found (<em>p</em> > 0.05). However, the results suggested that use of experiential-based videos and video-based tools may in fact be more beneficial for students who are weaker in critical thinking and self-efficacy, to gain a better understanding of their learning.</p><p> </p>

The Influence of Reflective Self-Explanations on Problem-Solving Performance

In this study, the effects of reflective self-explanations on conceptual understanding and problem solving are investigated in a domain of computer programming. After completing a multiple-choice test, 33 students were asked to reflect on and explain why their answers were correct or incorrect. Groups were divided for post hoc analysis based on the degree of reflective self-explanation and prior knowledge. Results indicated that students who actively engaged in the reflective self-explanation process performed better in problem solving tasks with minimal guidance. Prior knowledge was critical for effective reflection, especially recalling principles. Although low prior knowledge students tended to persist with their faulty mental models, self-explanation activities helped them to repair their models. The relationship between reflective self-explanation and prior knowledge is discussed.

Contributions of Self-Explanation to Comprehension of High- and Low-Cohesion Texts

This study examined how the contribution of self-explanation to science text comprehension is affected by the cohesion of a text at a local level. Psychology undergraduates read and self-explained a science text with either low or high local cohesion. Local cohesion was manipulated by the presence or absence of connectives and referential words or phrases that explicitly link successive sentences. After the self-explanation activity, participants answered open-ended comprehension questions about the text. Participants in the high local cohesion condition produced higher quality explanations, including more local bridging self-explanations, than those in the low local cohesion condition. However, these explanations, although higher in quality, did not improve comprehension. Performance on text-based comprehension questions was better in the low local cohesion condition. In addition, the correlation between self-explanation quality and comprehension performance was generally higher in the low local cohesion condition compared to the high local cohesion condition, even after factoring out participants' level of topic-relevant knowledge. These data suggest that the contribution of self-explanation to comprehension is larger when the text lacks certain cues that facilitate making connections between successive ideas in a text. Further, the results imply that a key contribution of self-explanation to text comprehension is to induce active inference processes whereby readers fill in conceptual gaps in challenging texts.

Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations

Learning with multiple representations is usually employed in order to foster understanding. However, it also imposes high demands on the learners and often does not lead to the expected results, especially because the learners do not integrate the different representations. Thus, it is necessary to support the learners’ self-explanation activity, which concerns the integration and understanding of multiple representations. In the present experiment, we employed multi-representational worked-out examples and tested the effects of two types of self-explanation prompts as help procedures for integrating and understanding multiple representations. The participants (N = 62) learned about probability theory under three conditions: (a) open self-explanation prompts, (b) self-explanation prompts in an assistance-giving-assistance-withholding procedure (assisting self-explanation prompts), or (c) no prompts (control group). Both types of self-explanation prompts fostered procedural knowledge. This effect was mediated by self-explanations directed to domain principles. Conceptual knowledge was particularly fostered by assisting self-explanation prompts which was mediated by self-explanations on the rationale of a principle. Thus, for enhancing high-quality self-explanations and both procedural knowledge and conceptual understanding, we conclude that assisting self-explanation prompts should be provided. We call this the assisting self-explanation prompt effect which refers to the elicitation of high-quality self-explanations and the acquisition of deep understanding.

Computer-supported example-based learning: When instructional explanations reduce self-explanations

We investigated whether the findings from worked-out example research on the effects of self-explanation prompts and on instructional explanations can be generalized to other example types – in this case: solved example problems. Whereas worked-out examples consist of a problem formulation, solution steps, and the final solution, solved example problems merely provide the problem formulation and the solution. We employed a first module of a computer-based environment for student teachers presenting solved example problems from which they learned how to select and to design worked-out examples for high school students. The participants (47 student teachers for German low- and medium-track schools and 33 student teachers for high-track schools) were randomly assigned to the cells of our 2 × 2 design (with vs. without self-explanation prompts; with vs. without instructional explanations). The effects of the different program versions on objective and subjective learning outcomes, on the perceived helpfulness of the program, and on the learning time were analyzed. As learning process data, the written and spoken self-explanations were assessed. The following main results were obtained: Particularly self-explanation prompts had favorable effects on learning outcomes, whereas instructional explanations can reduce the student teacher’s self-explanation activities and thereby the learning outcomes. Whereas “objectively” the most favorable learning outcomes were obtained when only self-explanation prompts were employed, the student teachers perceived their learning outcome best when only instructional explanations were provided. It can be concluded that the status of self-explanation prompts and of instructional explanations is comparable irrespective of the example type presented for learning.

Learning From Examples: Fostering Self-Explanations in Computer-Based Learning Environments

Cognitive skills acquisition involves developing the ability to solve problems in knowledge-rich task domains, and is particularly important for any individual attempting to meet the challenges of our modern, knowledge-driven economy. This type of economy argues for reconceptualizing cognitive skills acquisition as a lifelong process. Research has shown that worked-out examples are the key to initial cognitive skill acquisition and, therefore, critical to lifelong learning. The extent to which learners' profit from the study of examples, however, depends on how well they explain the solutions of the examples to themselves. This paper discusses our own research on different types of computer-based learning environments that indirectly foster self-explanations by (a) fostering anticipative reasoning, (b) supporting self-explanations during the transition from example study to problem solving, and (c) supporting self-explanation activities with instructional explanations. It also discusses ways of leveraging new computer and video technologies to enhance these environments by representing problem situations and their concepts dynamically. The paper concludes by suggesting that these learning environments, if employed successfully, can encourage systematic, lifelong learning.