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Presenting novices with examples and problems is an effective and efficient way to acquire new problem-solving skills. Nowadays, examples and problems are increasingly presented in computer-based learning environments, in which learners often have to self-regulate their learning (i.e., choose what type of task to work on and when). Yet, it is questionable how novices self-regulate their learning from examples and problems, and to what extent their choices match with effective principles from instructional design research. In this study, 147 higher education students had to learn how to solve problems on the trapezoidal rule. During self-regulated learning, they were free to select six tasks from a database of 45 tasks that varied in task format (video examples, worked examples, practice problems), complexity level (level 1, 2, 3), and cover story. Almost all students started with (video) example study at the lowest complexity level. The number of examples selected gradually decreased and task complexity gradually increased during the learning phase. However, examples and lowest level tasks remained relatively popular throughout the entire learning phase. There was no relation between students' total score on how well their behavior matched with the instructional design principles and learning outcomes, mental effort, and motivational variables.
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Problem-solving tasks form an important part of (higher education) curricula, especially in STEM-domains. For learners with little or no prior knowledge (novices), an effective way to learn new problem-solving tasks is by studying examples. These can be written out step-by-step solution procedures of a problem or teachers’ demonstrations of how to solve a problem. Nowadays, video examples are increasingly common. Moreover, students increasingly acquire problem-solving skills via computer-based learning environments in which examples and practice problems are presented. However, it is an open question how examples and practice problems can be best sequenced to foster novices’ motivation and learning outcomes. Moreover, relatively little is known about how (well) novices can self-regulate their learning with examples and practice problems. Both questions were addressed in this dissertation. Results showed that studying examples or alternating examples and practice problems, resulted in higher learning outcomes attained with less effort investment and more confidence in one's abilities than solving practice problems only. Moreover, starting with an example prior to practice problem solving resulted in more confidence in one's abilities and less effort investment than the other way around. When novices could select examples and practice problems themselves, they made choices that corresponded quite well with principles for effective sequencing known from instructional design research. Perhaps for that reason, instructing students on effective instructional design principles did not increase self-regulated learning outcomes. However, caution is needed when implementing self-regulated learning: even after instruction on effective principles, there still was room for improvement in students' task selections.
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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.
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Recent research has shown that example study only (EE) and example-problem pairs (EP) were more effective (i.e., higher test performance) and efficient (i.e., attained with less effort invested in learning and/or test tasks) than problem-example pairs (PE) and problem solving only (PP). We conducted two experiments to investigate how different example and problem-solving sequences would affect motivational (i.e., self-efficacy, perceived competence, and topic interest) and cognitive (i.e., effectiveness and efficiency) aspects of learning. In Experiment 1, 124 technical students learned a mathematical task with the help of EEEE, EPEP, PEPE, or PPPP and then completed a posttest. Students in the EEEE Condition showed higher posttest performance, self-efficacy, and perceived competence, attained with less effort investment, than students in the EPEP and PPPP Condition. Surprisingly, there were no differences between the EPEP and PEPE Condition on any of the outcome measures. We hypothesized that, because the tasks were relevant for technical students, starting with a problem might not have negatively affected their motivation. Therefore, we replicated the experiment with a different sample of 81 teacher training students. Experiment 2 showed an efficiency benefit of EEEE over EPEP, PEPE, and PPPP. However, only EEEE resulted in greater posttest performance, self-efficacy, and perceived competence than PPPP. We again did not find any differences between the EPEP and PEPE Condition. These results suggest that, at least when short training phases are used, studying examples (only) is more preferable than problem solving only for learning. Moreover, this study showed that example study (only) also enhances motivational aspects of learning whereas problem solving only does not positively affect students’ motivation at all.
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Voorbeelden en onderwijs lijken onlosmakelijk met elkaar verbonden. Het gebruik van voorbeelden is vaak zo vanzelfsprekend, dat zelfs in didactische opleidingen niet altijd aandacht wordt besteed aan de voorwaarden voor een optimal gebruik ervan. Voorbeelden blijken echter niet automatisch tot meer kennis en beter begrip te leiden. Leren van en door voorbeelden vereist bewuste aandacht en doet een beroep op analoog redeneren. Er is veel onderzoek gedaan naar de centrale rol van analoog redeneren in leren. De hieruit voortgekomen kennis en inzichten lijken echter nog niet algemeen bekend bij docenten noch breed geïmplementeerd in het onderwijs, zo komt ook naar voren uit een verkennend onderzoek aan De Haagse Hogeschool. Dit artikel vormt een eerste aanzet om kennis en inzichten in analoog redeneren en in het effectief gebruik van voorbeelden bredere bekendheid te geven. De aanbevelingen in het artikel zijn bedoeld om docenten te inspireren en uit te dagen. Abstract. The use of examples for teaching purposes would seem an obvious choice for teachers. This might be the reason why even courses intended to instruct teachers in their future profession sometimes skip over ways to make effective use of examples. However, research has shown that the use of examples does not automatically enhance a learner’s knowledge and understanding. Learning from examples requires conscious effort and attention and calls for analogical reasoning. Although the key role played by analogical reasoning in learning has been widely investigated, an exploratory study conducted among lecturers at The Hague University of Applied Sciences showed that not that many of them were familiar with the findings of these studies nor were these findings featured in their teaching. This article is an attempt to promote the acquisition of scientific knowledge and insights into analogical reasoning and the effective use of examples. The recommendations provided here are meant to inspire and challenge teachers.
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Mixing examples of different categories (interleaving) has been shown to promote inductive learning as compared with presenting examples of the same category together (massing). In three studies, we tested whether the advantage of interleaving is exclusively due to the mixing of examples from different categories or to the temporal gap introduced between presentations. In addition, we also tested the role of working memory capacity (WMC). Results showed that the mixing of examples might be the key component that determines improved induction. WMC might also be involved in the interleaving effect: participants with high spans seemed to profit more than participants with low spans from interleaved presentations. Our findings have relevant implications for education. Practice schedules should be individually customised so society as a whole can profit from differences between learners. (PsycINFO Database Record (c) 2017 APA, all rights reserved)
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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.
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The main result of this effectiveness study is that a reading program with a focus on students’ poetry reading processes, based on observational learning via eye movement modeling examples, can improve students’ reading comprehension for different text types. In a pretest-posttest design with an experimental group (ten classes) and a control group (five classes), students’ self-efficacy regarding their own reading process and their reading comprehension were measured. Over a six-week period, teachers of Dutch and their students worked with the six experimental lessons, instead of the regular reading program: students observed and evaluated contrasting peer reading processes, reflected on differences with their own reading process, and then they practiced aspects of a deep reading process. The program resulted in significant progress in the reading comprehension of “expository texts” (ES = .66), “short stories” (ES = .66), and especially “poetry” (ES = .81). Furthermore, the self-efficacy test results show that students in the experimental condition experienced significantly more learning effect after the intervention period than those in the control group. Moreover, based on the learning reports, evaluation tasks and interviews, it appears that the participants in the innovative program have become aware of their reading and how they improved their performance.
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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 improve self-regulated learning from examples and practice problems.
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Design and research are two fields of knowledge that each has its traditions, methods, standards and practices. These two worlds appear to be quite separate, with researchers investigating what exists, and designers visualising what could be. This book builds a bridge between both worlds by showing how design and research can be integrated to develop a new field of knowledge. This book contains 22 inspiring reflections that demonstrate how the unique qualities of research (aimed at studying the present) and design (aimed at developing the future) can be combined. This book shows that the transdisciplinary approach is applicable in a multitude of sectors, ranging from healthcare, urban planning, circular economy, and the food industry. Arranged in five parts, the book offers a range of illustrative examples, experiences, methods, and interpretations. Together they make up the characteristic of a mosaic, each piece contributing a part of the complete picture, and all pieces together offering a multi-facted perspective of what applied design research is, how it is implemented and what the reader can expect from it.
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