Fostering historical knowledge and thinking skills using hypermedia learning environments: The role of self-regulated learning

In this study, we examined how high-school students utilized a hypermedia learning environment (HLE) to acquire declarative knowledge of a historical topic, as well as historical thinking skills. In particular, we were interested in whether self-regulated learning (SRL; Winne & Hadwin, 1998; Zimmerman, 2000) processing was related to the acquisition of declarative knowledge and historical thinking. We found that, using the HLE, participants did learn from pretest to posttest, and that they most often engaged in strategy use SRL processes. However, the frequency of participant use of planning SRL processes, not strategy use, was predictive of learning. This study has implications for how educators use HLEs to foster historical thinking skills, and suggests that scaffolding planning skills may facilitate students’ use of computers as cognitive and metacognitive tools for learning (Azevedo, 2005; Lajoie, 2000).     

Can the use of cognitive and metacognitive self-regulated learning strategies be predicted by learners’ levels of prior knowledge in hypermedia-learning environments?

Research on self-regulated learning (SRL) in hypermedia-learning environments is a growing area of interest, and prior knowledge can influence how students interact with these systems. One hundred twelve (N = 112) undergraduate students’ interactions with MetaTutor, a multi-agent, hypermedia-based learning environment, were investigated, including how prior knowledge affected their use of SRL strategies. We expected that students with high prior knowledge would engage in significantly more cognitive and metacognitive SRL strategies, engage in different sequences of SRL strategies, spend more time engaging in SRL processes, and visit more pages that were relevant to their sub-goals than students with low prior knowledge. Results showed significant differences in the total use of SRL strategies between prior knowledge groups, and more specifically, revealed significant differences in the use of each metacognitive strategy (e.g., judgment of learning), but not each cognitive strategy (e.g., taking notes) between prior knowledge groups. Results also revealed different sequences of use of SRL strategies between prior knowledge groups, and that students spent different amounts of time engaging in SRL processes; however, all students visited similar numbers of relevant pages. These results have important implications on designing multi-agent, hypermedia environments; we can design pedagogical agents that adapt to students’ learning needs, based on their prior knowledge levels.     

Mining students’ inquiry actions for understanding of complex systems

This study lies at an intersection between advancing educational data mining methods for detecting students’ knowledge-in-action and the broader question of how conceptual and mathematical forms of knowing interact in exploring complex chemical systems. More specifically, it investigates students’ inquiry actions in three computer-based models of complex chemical systems when their goal is to construct an equation relating physical variables of the system. The study’s participants were 368 high-school students who interacted with the Connected Chemistry (CC1¹) curriculum and completed identical pre- and post-test content knowledge questionnaires. The study explores whether and how students adapt to different mathematical behaviors of the system, examines how these explorations may relate to prior knowledge and learning in terms of conceptual and mathematical models, as well as components relating to understanding systems. Students’ data-collection choices were mined and analyzed showing: (1) In about half the cases, mainly for two out of the three models explored, students conduct mathematically-astute (fit) explorations; (2) A third of the students consistently adapt their strategies to the models’ mathematical behavior; (3) Fit explorations are associated with prior conceptual knowledge, specifically understanding of the system as complex, however, the three explorations’ fitness is predicted by the understanding of distinct sets of systems’ components; (4) Fit explorations are only somewhat associated with learning along complementary dimensions. These results are discussed with respect to 1) the importance of a conceptual understanding regarding individual system elements even when engaged in large-scale quantitative problem solving, 2) how distinct results for the different models relate to previous literature on conceptual understanding and particular affordances of the models, 3) the importance of engaging students in creating mathematical representations of scientific phenomena, as well as 4) educational applications of these results in learning environments.     

Measuring critical components of digital literacy and their relationships with learning

The growing prominence of the Internet, and other digital environments, as educational tools requires research regarding learners' digital literacy. We argue that two critical aspects of digital literacy are the ability to effectively plan and monitor the efficacy of strategies used to search and manage the wealth of information available online, and the knowledge to appropriately vet and integrate those information sources. Therefore, digital literacy requires effective self-regulated learning (SRL) skills, and availing epistemic cognition (EC). Although numerous researchers and scholars have examined the role of SRL in online learning (e.g., Efklides, 2011; Lee and Tsai, 2010, Williams and Hellman, 2004, Winters et al., 2008), there is a need for additional empirical research on how SRL and EC interact, and relate to learning in digital environments. In this study, we used a powerful, but little-used data collection methodology, think-aloud protocol (TAP) analysis, to investigate the relations among SRL, EC, and learning gains with 20 college students who studied vitamins on the Internet. We also contributed to the literature by exploring alternative techniques for preparing, analyzing, and representing these data, accounting for the strengths and challenges of TAPs. We found that, on average, participants did increase their understanding as a result of learning with the Internet, and that a data-driven approach to understanding relations among SRL, EC, and learning yielded the most powerful representation of these phenomena. Our study has implications for future research on digital literacy using TAPs, as well as the relative contribution of SRL and EC, as aspects of digital literacy, to online learning.     

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.     

The interplay between cognitive and motivational variables in a supportive online learning system for secondary physical education

A path model was used to test the unique and interactive effects of cognitive and motivational variables when learning in a supportive online learning system, Collaborative Inquiry System (CIS). In this student-centered learning environment, students interact with computer simulations and are assisted by online scaffolds intended to help them learn complex scientific concepts. The present study also explored the relationships between students’ motivational, cognitive, and metacognitive strategy use and online performance. In total, 178 tenth-grade students participated in the study. The statistical analyses revealed that students’ learning goals and cognitive preferences predicted metacognitive strategy use and later influenced their performance. Prior knowledge is a predictor of neither metacognitive strategy use nor learning goals and need for cognition, but is nevertheless an important determinant of online performance. Discussions on how to accommodate the different needs of students with varying levels of prior knowledge, goal orientation, and cognitive preference in a supportive learning system are provided based on the results.     

The effect of computer science on physics learning in a computational science environment

College and high-school students face many difficulties when dealing with physics formulas, such as a lack of understanding of their components or of the physical relationships between the two sides of a formula. To overcome these difficulties some instructors suggest combining simulations' design while learning physics, claiming that the programming process forces the students to understand the physical mechanism activating the simulation. This study took place in a computational-science course where high-school students programmed simulations of physical systems, thus combining computer science (CS) and mathematics with physics learning. The study explored the ways in which CS affected the students' conceptual understanding of the physics behind formulas. The major part of the analysis process was qualitative, although some quantitative analysis was applied as well. Findings revealed that a great amount of the time was invested by the students on representing their physics knowledge in terms of computer science. Three knowledge domains were found to be applied: structural, procedural and systemic. A fourth domain which enabled reflection on the knowledge was found as well, the domain of execution. Each of the domains was found to promote the emergence of knowledge integration processes (Linn & Eylon, 2006, 2011), thus promoting students’ physics conceptual understanding. Based on these findings, some instructional implications are discussed.     

Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge

Students learn new instructions well by building on relevant prior knowledge, as it affects how instructors and students interact with the learning materials. Moreover, studies have found that good prior knowledge can enable students to attain better learning motivation, comprehension, and performance. This suggests it is important to assist students in obtaining the relevant prior knowledge, as this can enable them to engage meaningfully with the learning materials. Tests are often used to help instructors assess students’ prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to each student, and this may mean that students are unable to realize their own individual weaknesses. To address this problem, instructors can diagnose the test results to provide more detailed information to each student, but this is obviously a time-consuming process. Therefore, this study proposes a testing-based diagnosis system to assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken. Furthermore, an experiment was conducted to evaluate the effectiveness of the proposed approach in an interdisciplinary course, since several studies have indicated that students learn more and better in such courses when applying relevant prior knowledge to what they are learning. The experimental results show that the developed system is able to effectively diagnose students’ prior knowledge and enhance their learning motivation and performance on an interdisciplinary course. In addition, two diagnostic evaluations were also conducted to assess whether the diagnoses given by the system were consistent with the decisions of experts. The results demonstrate that the proposed system can effectively assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken, since the diagnoses produced by the system were broadly consistent with those of experts.     

Hypermedia learning and prior knowledge: domain expertise vs. system expertise

Abstract Prior knowledge is often argued to be an important determinant in hypermedia learning, and may be thought of as including two important elements: domain expertise and system expertise. However, there has been a lack of research considering these issues together. In an attempt to address this shortcoming, this paper presents a study that examines how domain expertise and system expertise influence students' learning performance in, and perceptions of, a hypermedia system. The results indicate that participants with lower domain knowledge show a greater improvement in their learning performance than those with higher domain knowledge. Furthermore, those who enjoy using the Web more are likely to have positive perceptions of non-linear interaction. Discussions on how to accommodate the different needs of students with varying levels of prior knowledge are provided based on the results.     

Constructing and evaluating online goal-setting mechanisms in web-based portfolio assessment system for facilitating self-regulated learning

The purpose of the present study was to construct goal-setting mechanisms in a web-based portfolio assessment system (WBPAS), based on the self-regulated learning (SRL) process proposed by Zimmerman, and to examine effects of these mechanisms on SRL. The participants were two classes of 11th graders taking the website design class in a vocational high school. The participants were assigned randomly to either an experimental group (n = 40) learning with a WBPAS or a control group (n = 41) learning with a paper-based portfolio. The study results revealed the following: a) the quality of goal-setting mechanisms may facilitate SRL. b) Students setting learning goals with the WBPAS demonstrated significantly better SRL than students setting learning goals with the paper-based portfolio.     

Monitoring, planning, and self-efficacy during learning with hypermedia: The impact of conceptual scaffolds

Self-report data and think-aloud data from 37 undergraduates were used to examine the impact of conceptual scaffolds on self-efficacy, monitoring, and planning during learning with a commercial hypermedia environment. Participants, randomly assigned to either the No Scaffolding (NS) or Conceptual Scaffolding (CS) condition, used a hypermedia environment for 30 min to learn about the circulatory system. Think-aloud data collected during this learning task was used to measure participants’ self-regulated learning (SRL) with hypermedia. Additionally, participants completed a self-efficacy questionnaire at three points during the learning task (immediately prior to the 30-min hypermedia learning task, 10 min into the learning task, and 20 min into the learning task). Results indicated that participants from both conditions reported higher levels of self-efficacy immediately before the hypermedia learning task, and that they decreased their use of SRL processes related to monitoring as they progressed through the hypermedia learning task. In addition, results also indicated that participants in the CS condition used, on average, more SRL processes related to planning during the hypermedia learning task than participants in the NS condition.     

Interplay of prior knowledge,self‐regulation and motivation in complex multimedia learning environments

This study examined the direct and indirect effects of medical clerkship students' prior knowledge, self‐regulation and motivation on learning performance in complex multimedia learning environments. The data from 386 medical clerkship students from six medical schools were analysed using structural equation modeling. The structural model revealed that medical students' prior knowledge directly positively affected their learning outcome, self‐efficacy and performance approach goal orientation. The learners' self‐regulation had a significant positive direct effect on learning outcome. The learners' mastery goal orientation directly affected their learning outcome. Interestingly, inconsistent with our hypothesis, the learners' performance approach goal orientation showed a significant negative direct effect on learning outcome, and performance avoidance goal orientation had a significant positive effect on learning outcome. These findings help develop a more comprehensive understanding of the role of individual characteristics on learning performance of complex tasks in multimedia learning environments.     

Understanding mobile learning from the perspective of self‐regulated learning

Cognizant of the research gap in the theorization of mobile learning, this paper conceptually explores how the theories and methodology of self‐regulated learning (SRL), an active area in contemporary educational psychology, are inherently suited to address the issues originating from the defining characteristics of mobile learning: enabling student‐centred, personal, and ubiquitous learning. These characteristics provide some of the conditions for learners to learn anywhere and anytime, and thus, entail learners to be motivated and to be able to self‐regulate their own learning. We propose an analytic SRL model of mobile learning as a conceptual framework for understanding mobile learning, in which the notion of self‐regulation as agency is at the core. The rationale behind this model is built on our recognition of the challenges in the current conceptualization of the mechanisms and processes of mobile learning, and the inherent relationship between mobile learning and SRL. We draw on work in a 3‐year research project in developing and implementing a mobile learning environment in elementary science classes in Singapore to illustrate the application of SRL theories and methodology to understand and analyse mobile learning.     

The double-edged effects of explanation prompts

Explanation prompts usually foster conceptual understanding. However, it has been claimed within cognitive load theory that prompts can take cognitive load to the upper limit when learning complex contents. Under such circumstances, prompts focusing the learners’ attention on specific aspects (e.g., conceptual aspects such as elaborations on domain principles) might have some costs: Other important aspects (e.g., procedural aspects such as how to calculate) cannot be processed deeply. Thus, we expected that conceptually-oriented explanation prompts would foster the detailedness of explanations, the number of elaborations on domain principles, and conceptual knowledge. In addition, we tested the influence of such prompts on the number of calculations performed during learning and procedural knowledge. We conducted an experiment in which we employed conceptually-oriented explanation prompts in a complex e-learning module on tax law. Tax law university students (N = 40) worked on this e-learning module under two conditions: (a) conceptually-oriented explanation prompts, (b) no prompts. The prompts led to double-edged effects: positive effects on the detailedness of explanations and on the number of elaborations on domain principles, as well as on conceptual knowledge and simultaneously negative effects on the number of calculations performed during learning as well as on procedural knowledge.     

Providing effective feedback, monitoring and evaluation to on-line collaborative learning discussions

Learning and knowledge building have become critical competences for people in the knowledge society era. In this paper, we propose a sociolinguistic dialogue model for understanding how learning evolves and how cognitive process is constructed in on-line discussions. The knowledge extracted from this model is used to assess participation behavior, knowledge building and performance. The ultimate purpose is to provide effective feedback, evaluation and monitoring to the discussion process. Seven hundred students from the Open University of Catalonia in Spain participated in this study. Results showed that learning and knowledge building may be greatly enhanced by presenting selected knowledge to learners as for their particular skills exhibited during interaction. In addition, this valuable provision of information is used as a meta cognitive tool for tutors and moderators for monitoring and evaluating the discussion process more conveniently. This contribution presents our conceptual model for interaction management as well as key design guidelines and evaluation results. Implications of this study are remarked and further research directions are proposed.     

The impact of team‐based learning on students with different self‐regulated learning abilities

Team‐based learning (TBL) stresses applying knowledge rather than absorbing knowledge in class; studies have investigated the use of TBL and its merits in different teaching courses (e.g., medical science and business). TBL is most effective when students learn autonomously before class. However, the ability of autonomous learning is highly associated with the ability of self‐regulated learning (SRL); most importantly, not every student possesses good (or high) SRL ability. Nevertheless, few studies have compared the effectiveness of TBL in students with different SRL abilities. To address this issue, this study analyzed approximately 90 students, whose course teaching involves office application software (Microsoft Excel). This study also developed an online TBL system (called Online TBL) to facilitate performing TBL and to collect the learning behaviours of students with different (high or low) SRL abilities on each TBL stage. The analytical results show that compared with the low‐SRL students, the high‐SRL students were more prepared for class because they spent more reviewing material and had better scores on personal uploaded Excel and Individual Readiness Assurance Test. From the feedback of the peer evaluation, the results also show that the high‐SRL students received more credits than the low‐SRL students did. The questionnaire survey revealed that both low‐SRL and high‐SRL students had a favourable impression of TBL. Further discussion is given to explain the above results.     

The comparative effect of individually-constructed vs. collaboratively-constructed computer-based concept maps

The researchers investigated the comparative effects of individually-constructed and collaboratively-constructed computer-based concept mapping on middle school science concept learning. One hundred and sixty one students completed the entire study. Using prior science performance scores to assure equivalence of student achievement across groups, students were assigned to three groups: a self-selected study strategy group, an individual-concept mapping group, and a collaborative pairs – concept mapping group. Collaboratively and individually-constructing computer-based concept maps had equally positive effects on seventh grade middle school science concept learning as measured on a comprehension test. However, the students who collaboratively constructed concept maps created significantly higher quality concept maps than those who individually constructed concept maps indicating deeper conceptual understanding.     

Social gaming is inSIDE: Impact of anonymity and group identity on performance in a team game-based learning environment

The present experiment aimed to determine how quiz performance in a team game-based learning environment can be predicted from the Social Identity model of Deindividuation Effects (SIDE). According to this model, anonymity influences social behavior by accentuating the salience of group identity and reducing interpersonal differences, leading to greater group identification and motivation to work for one's own group. As these effects could lead to higher cognitive performance, the goal of the present research was to extend predictions based on the SIDE model on performance in online game-based learning environments. After measuring their prior computing knowledge, 343 Master Degree students were placed in virtual teams on a trivial criterion to perform a series of online quizzes about computing and the Internet. An anonymous (or individuated) username was attributed to each team member to connect to the online learning environment, and information about comparison between teams was used to manipulate the degree of salience of group identity (high versus low). As predicted by the SIDE model, anonymity boosted performance when group identity was salient, but only for students with low prior knowledge. Unexpectedly, it was also found that anonymity boosted the performance of students with high prior knowledge when group identity was not salient. A similar pattern was found for perceived mastery of computing and the Internet. Theoretical and practical implications of the SIDE model are discussed, and specifically its application to social gaming to optimize online learning.     

Effects of prior knowledge on learning from different compositions of representations in a mobile learning environment

Two experiments examined the effects of prior knowledge on learning from different compositions of multiple representations in a mobile learning environment on plant leaf morphology for primary school students. Experiment 1 compared the learning effects of a mobile learning environment presenting text and photos of plants on a tablet PC, either in combination with or without real plants in the physical environment. Results indicated that there were no interactions between prior knowledge and experimental condition. Students who learned with tablet PCs only outperformed students who additionally learned with real plants on a comprehension and an application test. In addition, high prior knowledge students outperformed low prior knowledge students on both tests. To investigate whether these effects were caused by the specific characteristics of the combination of photos of real plants and real plants, Experiment 2 compared the differential effects of prior knowledge on learning with the combination of texts, photos and real plants to a combination in which the photos were replaced by schematic hand drawings. Results indicated that both low and high prior knowledge students, who learned with the combination of texts, schematic hand drawings and real plants performed better on a comprehension and an application test. High prior knowledge students performed better on both tests. It is concluded that the number and type of representations used is critical for the effectiveness of mobile learning environments.     

Effects of simulation‐based learning on students' statistical factual,conceptual and application knowledge

The purpose of this study was to (1) examine the effects of a storyline on learners' factual, conceptual and application knowledge with the use of a simulation for teaching introductory statistical skills and to (2) explore students' subjective enjoyment of various learning activities often used in statistics education. In order to conduct the study, two versions of a simulation were developed that differed in the presence or absence of a storyline attribute. Sixty‐four graduate students were randomly assigned to one of the two intervention conditions. Both intervention groups demonstrated significantly higher learning gains after interacting with the simulation. Particularly, both simulation‐based interventions had a positive significant effect on the acquisition of application knowledge and skills. However, no significant differences between the intervention groups on any learning outcome explored in the study were found. Results also showed that students rated the simulation used in the study as a more enjoyable learning activity in comparison to reading a textbook, lecture or teamwork. Students from the simulation without a storyline intervention reported higher enjoyment than the other intervention group. Implications of the findings for understanding the instructional benefits and shortcomings of embedding a storyline in digital learning content are discussed.