Eliciting explanations: Constraints on when self-explanation aids learning

Shifting focus to online learning during the COVID-19 pandemic in Saudi Universities: Challenges and opportunities

Since 2020, Saudi administrations have provisionally closed educational institutions to mitigate the spread of COVID-19. At the time, employing technology was imperative to accelerate learning efforts and offer methods of enhancing interactions between learners and among learners and tutors. In this review, I first describe the e-learning systems that were used in higher education before the pandemic. Then, I investigate the impact of COVID-19 on Saudi higher education and how universities and public educational institutions responded to the pandemic. In the conclusion, I argue that policymakers, university sectors, and syllabi developers should unify national e-learning strategies, integrate technology in a systematic way, and design e-learning curricula to meet the needs of an ever-advancing world and revolutionise the learning process.

Learning-by-Teaching Without Audience Presence or Interaction: When and Why Does it Work?

Teaching the contents of study materials by providing explanations to fellow students can be a beneficial instructional activity. A learning-by-teaching effect can also occur when students provide explanations to a real, remote, or even fictitious audience that cannot be interacted with. It is unclear, however, which underlying mechanisms drive learning by non-interactive teaching effects and why several recent studies did not replicate this effect. This literature review aims to shed light on when and why learning by non-interactive teaching works. First, we review the empirical literature to comment on the different mechanisms that have been proposed to explain why learning by non-interactive teaching may be effective. Second, we discuss the available evidence regarding potential boundary conditions of the non-interactive teaching effect. We then synthesize the available empirical evidence on processes and boundary conditions to provide a preliminary theoretical model of when and why non-interactive teaching is effective. Finally, based on our model of learning by non-interactive teaching, we outline several promising directions for future research and recommendations for educational practice.

Ways that nurse practitioner students self-explain during diagnostic reasoning

OBJECTIVES

An important step in mitigating the burden of diagnostic errors is strengthening diagnostic reasoning among health care providers. A promising way forward is through self-explanation, the purposeful technique of generating self-directed explanations to process novel information while problem-solving. Self-explanation actively improves knowledge structures within learners' memories, facilitating problem-solving accuracy and acquisition of knowledge. When students self-explain, they make sense of information in a variety of unique ways, ranging from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. The unique types of self-explanation present among nurse practitioner (NP) student diagnosticians have yet to be explored. This study explores the question: How do NP students self-explain during diagnostic reasoning?

METHODS

Thirty-seven Family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern U.S. university diagnosed three written case studies while self-explaining. Dual methodology content analyses facilitated both deductive and qualitative descriptive analysis.

RESULTS

Categories emerged describing the unique ways that NP student diagnosticians self-explain. Nine categories of inference self-explanations included clinical and biological foci. Eight categories of non-inference self-explanations monitored students' understanding of clinical data and reflect shallow information processing.

CONCLUSIONS

Findings extend the understanding of self-explanation use during diagnostic reasoning by affording a glimpse into fine-grained knowledge structures of NP students. NP students apply both clinical and biological knowledge, actively improving immature knowledge structures. Future research should examine relationships between categories of self-explanation and markers of diagnostic success, a step in developing prompted self-explanation learning interventions.

Diagnostic reasoning: relationships among expertise, accuracy, and ways that nurse practitioner students self-explain

OBJECTIVES

To improve diagnostic ability, educators should employ multifocal strategies. One promising strategy is self-explanation, the purposeful technique of generating self-directed explanations during problem-solving. Students self-explain information in ways that range from simple restatements to multidimensional thoughts. Successful problem-solvers frequently use specific, high-quality self-explanation types. In a previous phase of research, unique ways that family nurse practitioner (NP) students self-explain during diagnostic reasoning were identified and described. This study aims to (a) explore relationships between ways of self-explaining and diagnostic accuracy levels and (b) compare differences between students of varying expertise in terms of ways of self-explaining and diagnostic accuracy levels. Identifying high-quality diagnostic reasoning self-explanation types may facilitate development of more refined self-explanation educational strategies.

METHODS

Thirty-seven family NP students enrolled in the Doctor of Nursing Practice program at a large, Midwestern university diagnosed three written case studies while self-explaining. During the quantitative phase of a content analysis, associational and comparative data analysis techniques were applied.

RESULTS

Expert students voiced significantly more clinical and biological inference self-explanations than did novice students. Diagnostic accuracy scores were significantly associated with biological inference scores. Clinical and biological inference scores accounted for 27% of the variance in diagnostic accuracy scores, with biological inference scores significantly influencing diagnostic accuracy scores.

CONCLUSIONS

Not only were biologically focused self-explanations associated with diagnostic accuracy, but also their spoken frequency influenced levels of diagnostic accuracy. Educational curricula should support students to view patient presentations in terms of underlying biology from the onset of their education.

The ecological rationality of explanatory reasoning

There is growing evidence that explanatory considerations influence how people change their degrees of belief in light of new information. Recent studies indicate that this influence is systematic and may result from people's following a probabilistic update rule. While formally very similar to Bayes' rule, the rule or rules people appear to follow are different from, and inconsistent with, that better-known update rule. This raises the question of the normative status of those updating procedures. Is the role explanation plays in people's updating their degrees of belief a bias? Or are people right to update on the basis of explanatory considerations, in that this offers benefits that could not be had otherwise? Various philosophers have argued that any reasoning at deviance with Bayesian principles is to be rejected, and so explanatory reasoning, insofar as it deviates from Bayes' rule, can only be fallacious. We challenge this claim by showing how the kind of explanation-based update rules to which people seem to adhere make it easier to strike the best balance between being fast learners and being accurate learners. Borrowing from the literature on ecological rationality, we argue that what counts as the best balance is intrinsically context-sensitive, and that a main advantage of explanatory update rules is that, unlike Bayes' rule, they have an adjustable parameter which can be fine-tuned per context. The main methodology to be used is agent-based optimization, which also allows us to take an evolutionary perspective on explanatory reasoning.

Quality of Explanation as an Indicator of Fraction Magnitude Understanding

Students' explanations of their mathematical thinking and conclusions have become a greater part of the assessment landscape in recent years. With a sample of 71 fourth-grade students at risk for mathematics learning disabilities, we investigated the relation between student accuracy in comparing the magnitude of fractions and the quality of students' explanations of those comparisons, as well as the relation between those measures and scores on a criterion test: released fraction items from the National Assessment of Educational Progress. We also considered the extent to which reasoning and language contribute to the prediction. Results indicated a significant moderate correlation between accuracy and explanation quality. Commonality analyses indicated that explanation quality accounts for little variance in National Assessment of Educational Progress scores beyond what is accounted for by traditional measures of magnitude understanding. Implications for instruction and assessment are discussed.

Examining the effect of self-explanation on cognitive integration of basic and clinical sciences in novices

Several studies have shown that cognitive integration of basic and clinical sciences supports diagnostic reasoning in novices; however, there has been limited exploration of the ways in which educators can translate this model of mental activity into sound instructional strategies. The use of self-explanation during learning has the potential to promote and support the development of integrated knowledge by encouraging novices to elaborate on the causal relationship between clinical features and basic science mechanisms. To explore the effect of this strategy, we compared diagnostic efficacy of teaching students (n = 71) the clinical features of four musculoskeletal pathologies using either (1) integrated causal basic science descriptions (BaSci group); (2) integrated causal basic science descriptions combined with self-explanation prompts (SE group); (3) basic science mechanisms segregated from the clinical features (SG group). All participants completed a diagnostic accuracy test immediately after learning and 1-week later. The results showed that the BaSci group performed significantly better compared to the SE (p = 0.019) and SG groups (p = 0.004); however, no difference was observed between the SE and SG groups (p = 0.91). We hypothesize that the structure of the self-explanation task may not have supported the development of a holistic conceptual understanding of each disease. These findings suggest that integration strategies need to be carefully structured and applied in ways that support the holistic story created by integrated basic science instruction in order to foster conceptual coherence and to capitalize on the benefits of cognition integration.