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Novieten leren nieuwe probleem-oplostaken op een effectieve manier door het bestuderen van voorbeelden, zoals stap-voor-stap uitgeschreven oplossingsprocedures of een docent die (op video) voordoet en uitlegt hoe je een probleem oplost. Steeds vaker worden (video)voorbeelden en oefenproblemen ingezet in computer-gebaseerde leeromgevingen om studenten (zelfstandig) nieuwe probleem-oplostaken te leren. In dit proefschrift is onderzocht 1) hoe (video)voorbeelden en oefenproblemen aangeboden moeten worden aan novieten om hun motivatie en leerprestaties te bevorderen, en 2) hoe (goed) novieten leren van (video)voorbeelden en oefenproblemen als zij zelfstandig leertaken kiezen. Resultaten lieten zien dat het bestuderen van voorbeelden, eventueel afgewisseld met oefenproblemen, leidde tot hogere leerprestaties, behaald met minder moeite en meer vertrouwen in eigen kunnen, dan alleen oefenproblemen oplossen. Starten met een voorbeeld voorafgaand aan een oefenprobleem kostte minder moeite en gaf meer vertrouwen in het eigen kunnen dan andersom. Wanneer studenten zelf voorbeelden en oefenproblemen konden kiezen, kwamen hun keuzes relatief goed overeen met bekende principes voor het effectief leren van nieuwe probleem-oplostaken. Wellicht om die reden, leidde instructie over zulke principes voorafgaand aan zelfgestuurd leren, niet tot betere leeruitkomsten. Toch is voorzichtigheid geboden met het inzetten van zelfgestuurd leren: zelfs na instructie over effectieve principes was er ruimte voor verbetering in taakkeuzes.

This study investigated whether unsuccessful transfer of critical thinking (CT) would be due to recognition, recall, or application problems (cf. three-step model of transfer). In two experiments (laboratory: N = 196; classroom: N = 104), students received a CT-skills pretest (including learning, near transfer, and far transfer items), CT-instructions, practice problems, and a CT-skills posttest. On the posttest transfer items, students either (1) received no support, (2) received recognition support, (3) were prompted to recall acquired knowledge, or (4) received recall support. Results showed that CT could be fostered through instruction and practice: we found learning, near transfer, and (albeit small) far transfer performance gains and reduced test-taking time. There were no significant differences between the four support conditions, however, suggesting that the difficulty of transfer of CT-skills lies in problems with application/mapping acquired knowledge onto new tasks. Additionally, exploratory results on free recall data suggested suboptimal recall can be a problem as well.

In this article the author name ‘Agustin Ibanez’ was incorrectly written as ‘Augustin Ibanez’. The original article has been corrected.

BACKGROUND:Little is known about how conspiracy beliefs and health responses are interrelated over time during the course of the coronavirus disease 2019 (Covid-19) pandemic. This longitudinal study tested two contrasting, but not mutually exclusive, hypotheses through cross-lagged modeling. First, based on the consequential nature of conspiracy beliefs, we hypothesize that conspiracy beliefs predict an increase in detrimental health responses over time. Second, as people may rationalize their behavior through conspiracy beliefs, we hypothesize that detrimental health responses predict increased conspiracy beliefs over time.METHODS: We measured conspiracy beliefs and several health-related responses (i.e. physical distancing, support for lockdown policy, and the perception of the coronavirus as dangerous) at three phases of the pandemic in the Netherlands (N = 4913): During the first lockdown (Wave 1: April 2020), after the first lockdown (Wave 2: June 2020), and during the second lockdown (Wave 3: December 2020).RESULTS: For physical distancing and perceived danger, the overall cross-lagged effects supported both hypotheses, although the standardized effects were larger for the effects of conspiracy beliefs on these health responses than vice versa. The within-person change results only supported an effect of conspiracy beliefs on these health responses, depending on the phase of the pandemic. Furthermore, an overall cross-lagged effect of conspiracy beliefs on reduced support for lockdown policy emerged from Wave 2 to 3.CONCLUSIONS: The results provide stronger support for the hypothesis that conspiracy beliefs predict health responses over time than for the hypothesis that health responses predict conspiracy beliefs over time.

There is a need for effective methods to teach critical thinking. Many studies on other skills have demonstrated beneficial effects of practice that repeatedly induces retrieval processes (repeated retrieval practice). The present experiment investigated whether repeated retrieval practice is effective for fostering critical thinking skills, focusing on avoiding biased reasoning. Seventy-five students first took a pre-test. Subsequently, they were instructed on critical thinking and avoiding belief-bias in syllogistic reasoning and engaged in retrieval practice with syllogisms. Afterwards, depending on the assigned condition, they (1) did not engage in extra retrieval practice; (2) engaged in retrieval practiced a second time (week later); or (3) engaged in retrieval practiced a second (week later) and a third time (two weeks later). Two/three days after the last practice session, all participants took a post-test consisting of practiced tasks (to measure learning relative to the pre-test) and non-practiced (transfer) tasks. Results revealed no significant difference between the pretest and the posttest learning performance as judged by the mean total performance (MC-answers + justification), although participants were, on average, faster on the post-test than on the pre-test. Exploring performance on MC-answers-only suggested that participants did benefit from instruction/practice but may have been unable to justify their answers. Unfortunately, we were unable to test effects on transfer due to a floor effect, which highlights the difficulty of establishing transfer of critical thinking skills. To the best of our knowledge, this is the first study that addresses repeated retrieval practice effects in the critical thinking domain. Further research should focus on determining the preconditions of repeated retrieval practice effects for this type of tasks.

There is a need for effective methods to teach critical thinking (CT). One instructional method that seems promising is comparing correct and erroneous worked examples (i.e., contrasting examples). The aim of the present study, therefore, was to investigate the effect of contrasting examples on learning and transfer of CT-skills, focusing on avoiding biased reasoning. Students (N = 170) received instructions on CT and avoiding biases in reasoning tasks, followed by: (1) contrasting examples, (2) correct examples, (3) erroneous examples, or (4) practice problems. Performance was measured on a pretest, immediate posttest, 3-week delayed posttest, and 9-month delayed posttest. Our results revealed that participants’ reasoning task performance improved from pretest to immediate posttest, and even further after a delay (i.e., they learned to avoid biased reasoning). Surprisingly, there were no differences in learning gains or transfer performance between the four conditions. Our findings raise questions about the preconditions of contrasting examples effects. Moreover, how transfer of CT-skills can be fostered remains an important issue for future research.

There is a need for effective methods to teach critical thinking. Many studies on other skills have demonstrated beneficial effects of practice that repeatedly induces retrieval processes (repeated retrieval practice). The present experiment investigated whether repeated retrieval practice is effective for fostering critical thinking skills, focusing on avoiding biased reasoning. Seventy-five students first took a pre-test. Subsequently, they were instructed on critical thinking and avoiding belief-bias in syllogistic reasoning and engaged in retrieval practice with syllogisms. Afterwards, depending on the assigned condition, they (1) did not engage in extra retrieval practice; (2) engaged in retrieval practiced a second time (week later); or (3) engaged in retrieval practiced a second (week later) and a third time (two weeks later). Two/three days after the last practice session, all participants took a post-test consisting of practiced tasks (to measure learning relative to the pre-test) and non-practiced (transfer) tasks. Results revealed no significant difference between the pretest and the posttest learning performance as judged by the mean total performance (MC-answers + justification), although participants were, on average, faster on the post-test than on the pre-test. Exploring performance on MC-answers-only suggested that participants did benefit from instruction/practice but may have been unable to justify their answers. Unfortunately, we were unable to test effects on transfer due to a floor effect, which highlights the difficulty of establishing transfer of critical thinking skills. To the best of our knowledge, this is the first study that addresses repeated retrieval practice effects in the critical thinking domain. Further research should focus on determining the preconditions of repeated retrieval practice effects for this type of tasks.

Nowadays, students often practice problem-solving skills in online learning environments with the help of examples and problems. This requires them to self-regulate their learning. It is questionable how novices self-regulate their learning from examples and problems and whether they need support. The present study investigated the open questions (1) to what extent students' (novices) task selections align with instructional design principles and (2) whether informing them about these principles would improve their task selections, learning outcomes, and motivation. Higher education students (N = 150) learned a problem-solving procedure by fixed sequences of examples and problems (FS-condition), or by self-regulated learning (SRL). The SRL participants selected tasks from a database, varying in format, complexity, and cover story, either with (ISRL-condition) or without (SRL-condition) watching a video detailing the instructional design principles. Students' task-selection patterns in both SRL conditions largely corresponded to the principles, although tasks were built up in complexity more often in the ISRL-condition than in the SRL-condition. Moreover, there was still room for improvement in students' task selections after solving practice problems. The video instruction helped students to better apply certain principles, but did not enhance learning and motivation. Finally, there were no test performance or motivational differences among conditions. Although these findings might suggest it is relatively ‘safe’ to allow students to independently start learning new problems-solving tasks using examples and problems, caution is warranted: It is unclear whether these findings generalize to other student populations, as the students participating in this study have had some experience with similar tasks or learning with examples. Moreover, as there was still room for improvement in students' task selections, follow-up research should investigate how we can further

Does cognitive motivation influence how people gather and interpret information about COVID-19 and their adherence to measures? To address these questions, we conducted a longitudinal survey among European and American respondents. Wave 1 (N = 501) was conducted on March 27, 2020 and Wave 2 (N = 326) on July 1, 2020. We assessed COVID-19 knowledge, endorsement of COVID-19 conspiracy theories, media use, Need for Cognition (NC), Need for Cognitive Closure (NCC), and self-reported adherence to governmental measures taken. Results showed that nearly three-quarters of our respondents actively searched for information about COVID-19. Most at least once a day. Information seeking behaviour was not influenced by cognitive motivation (i.e., NC and NCC). However, cognitive motivation was related to (1) knowledge about COVID-19, (2) conspiracy rejection, and (3) change in knowledge over time. Respondents with more knowledge on COVID-19 also indicated to adhere more often to measures taken by their government. Self-reported adherence to measures was not influenced by cognitive motivation. Implications of these findings will be discussed.

The aim of this experiment was to examine the effect of different instructional strategies on student teachers’ confirmation bias. Confirmation bias refers to the selectivity in finding and using evidence that fits one’s own beliefs or hypotheses while neglecting evidence that is opposite to one’s own beliefs or hypotheses (Nickerson, 1998). Dutch student teachers (n = 141) took a confirmation bias pre-test and were then randomly assigned to three conditions; teaching on video (TOV), preparing to teach (PTT) and re-study (CC). All participants received text-based instruction on confirmation bias and how it can be mitigated. They also practised with confirmation bias tasks and they received feedback on their answers. Subsequently, participants in the TOV and PTT conditions prepared a lesson about the instructional content and in the TOV condition they taught this lesson on video. After the learning phase, TOV and PTT participants completed a social presence questionnaire. All participants completed an arousal questionnaire and a confirmation bias post-test and a transfer test. The results showed that confirmation bias was reduced to a similar extent in all conditions. Results also showed that the quality of the prepared lesson was highest for TOV participants suggesting they had gained better understanding of the confirmation bias than PTT participants. Furthermore, in contrast to our expectations, PTT participants reported highest social presence scores. TOV participants experienced higher arousal levels compared to CC participants. Transfer scores did not differ between conditions. We discuss theoretical explanations of the findings from the present study.

It is yet unclear which teaching methods are most effective for improving critical thinking (CT) skills and especially for the ability to avoid biased reasoning. Two experiments (laboratory: N = 85; classroom: N = 117), investigated the effect of practice schedule (interleaved/blocked) on students’ learning and transfer of unbiased reasoning, and whether it interacts with practice-task format (worked-examples/problems). After receiving CT-instructions, participants practiced in: (1) a blocked schedule with worked examples, (2) an interleaved schedule with worked examples, (3) a blocked schedule with problems, or (4) an interleaved schedule with problems. In both experiments, learning outcomes improved after instruction/practice. Surprisingly, there were no indications that interleaved practice led to better learning/transfer than blocked practice, irrespective of task format. The practice-task format did matter for novices’ learning: worked examples were more effective than low-assistance practice problems, which demonstrates –for the first time–that the worked-example effect also applies to novices’ learning to avoid biased reasoning.

Rationality is considered an important trait for functioning in modern society, as it is thought to be necessary for making judgments and decisions. Rationality aligns well with ambitions in higher education for teaching students to be critical thinkers and decision makers. The short-form Comprehensive Assessment of Rational Thinking (CART) might therefore be suitable for measuring this ability in higher education. Subtest total scores are used and interpreted as distinguishable units of measurement, representing single subconstructs within the concept of rationality. In this study, we explored whether the subtests can indeed be interpreted as unidimensional scales. We assessed the subtests’ dimensionality by performing a Mokken scale analysis using the performance data of 185 students of higher education on a Dutch translation of the short-form CART. For two subtests, all items formed a single ordinal scale. For three subtests, a subset of items formed a scale. For seven subtests, no items, nor any subset of items, formed a unidimensional scale. In its current form, then, not all short-form CART subtest scores could be meaningfully interpreted as measures of constituent parts of rationality, limiting its use for assessment in higher education.

This study investigated whether an instructional intervention can increase students’ self-regulated use of retrieval practice in a higher education classroom environment. A lab-experiment by Ariel and Karpicke (2017) revealed that strategy instructions improved students’ self-regulated use of retrieval practice and subsequent test performance. Our goal was to determine whether these effects generalize to a classroom environment using key concepts from marketing communication. We compared two groups on their self-regulated use of retrieval practice using an online environment. An experimental group (n = 58) received strategy instructions on retrieval practice and a control group (n = 58) received neutral instructions. Instructions were provided during sessions 1 and 2; no instructions were provided in a third, transfer session, measuring self-regulated use of retrieval practice. In sessions 1 and 2, no significant differences between groups were found. In the transfer session, the experimental group tested themselves more (Hypothesis 1) and displayed a larger number of (correct) retrieval attempts per key concept (Hypothesis 2) than the control group. No correlations were found with performance (Hypothesis 3). With our experiment, we took a first step in supporting students in their self-regulated use of retrieval practice in a classroom environment with complex materials.

Research suggests some sequences of examples and problems (i.e., EE, EP) are more effective (higher test performance) and efficient (attained with equal/less mental effort) than others (PP, sometimes also PE). Recent findings suggest this is due to motivational variables (i.e., self-efficacy), but did not test this during the training phase. Moreover, prior research used only short task sequences. Therefore, we investigated effects on motivational variables, effectiveness, and efficiency in a short (Experiment 1; four learning tasks; n = 157) and longer task sequence (Experiment 2; eight learning tasks; n = 105). With short sequences, all example conditions were more effective, efficient, and motivating than PP. With longer sequences, all example conditions were more motivating and efficient than PP, but only EE was more effective than PP. Moreover, EE was most efficient during training, regardless of sequence length. These results suggest that example study (only) is more effective, efficient, and more motivating than PP.

Cumulative assessment refers to interspersed testing in which each assessment covers all previous content and the mean assessments’ grade weighs in for the final exam grade. The effect of cumulative assessment on motivation and performance might differ between summative (i.e. assessment grades weigh in for the final exam grade) and formative (i.e. the assessments grades do not weigh in) variants. The present study explored this hypothesis in two field experiments in a higher education course (Exp 1: n = 102; Exp 2: n = 88). Each experiment used a single-factor, between-subjects design with type of cumulative assessment (i.e. summative vs formative) as independent variable and motivation (Exp 1: self-study time, topic interest, perceived competence; Exp 2: preparation time and self-efficacy) and performance (Exp 2: cumulative assessment performance; Exp1 and Exp2: final exam grade and delayed test performance) as dependent variables. The results of both experiments reinforced each other. In the summative condition, the final exam grade was higher than in the formative condition. However, when the summative assessments were discarded from the final grade, this difference disappeared. Also, in both experiments, the conditions did not differ on motivation measures. Theoretical and practical implications of our findings are discussed.

Research suggests some sequences of examples and problems (i.e., EE, EP) are more effective (higher test performance) and efficient (attained with equal/less mental effort) than others (PP, sometimes also PE). Recent findings suggest this is due to motivational variables (i.e., self-efficacy), but did not test this during the training phase. Moreover, prior research used only short task sequences. Therefore, we investigated effects on motivational variables, effectiveness, and efficiency in a short (Experiment 1; 4 learning tasks; n=157) and longer task sequence (Experiment 2; 8 tasks; n=105). With short sequences, all example conditions were more effective, efficient, and motivating than PP. With longer sequences, all example conditions were more motivating and efficient than PP, but only EE was more effective than PP. Moreover, EE was most efficient during training, regardless of sequence length. These results suggest that example study (only) is more effective, efficient and more motivating than PP.