Instructing students on effective sequences of examples and problems: Does self-regulated learning improve from knowing what works and why?

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

How do higher education students regulate their learning with video modeling examples, worked examples, and practice problems?

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

Acquiring problem-solving skills in higher education : Sequencing and self-regulated learning from examples and problems

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

Acquiring problem-solving skills in higher education : Sequencing and self-regulated learning from examples and problems

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

How do higher education students regulate their learning with video modeling examples, worked examples, and practice problems?.

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

Probleem-oplossen in het hoger onderwijs door (zelfgestuurd) leren van voorbeelden en oefenproblemen.: Artikel gebaseerd op proefschrift: Acquiring problem-solving skills in higher education: Sequencing and self-regulated learning from examples and problems.

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

The Role of Mental Effort in Fostering Self-Regulated Learning with Problem-Solving Tasks

Problem-solving tasks form the backbone of STEM (science, technology, engineering, and mathematics) curricula. Yet, how to improve self-monitoring and self-regulation when learning to solve problems has received relatively little attention in the self-regulated learning literature (as compared with, for instance, learning lists of items or learning from expository texts). Here, we review research on fostering self-regulated learning of problem-solving tasks, in which mental effort plays an important role. First, we review research showing that having students engage in effortful, generative learning activities while learning to solve problems can provide them with cues that help them improve self-monitoring and self-regulation at an item level (i.e., determining whether or not a certain type of problem needs further study/practice). Second, we turn to self-monitoring and self-regulation at the task sequence level (i.e., determining what an appropriate next problem-solving task would be given the current level of understanding/performance). We review research showing that teaching students to regulate their learning process by taking into account not only their performance but also their invested mental effort on a prior task when selecting a new task improves self-regulated learning outcomes (i.e., performance on a knowledge test in the domain of the study). Important directions for future research on the role of mental effort in (improving) self-monitoring and self-regulation at the item and task selection levels are discussed after the respective sections

Effects of different sequences of examples and problems on motivation and learning

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

Effects of different sequences of examples and problems on motivation and learning

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

Examples, practice problems, or both? Effects on motivation and learning in shorter and longer sequences

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