Unraveling the influence of domain knowledge during simulation-based inquiry learning

This study investigated whether the mere knowledge of the meaning of variables can facilitate inquiry learning processes and outcomes. Fifty-seven college freshmen were randomly allocated to one of three inquiry tasks. The concrete task had familiar variables from which hypotheses about their underlying relations could be inferred. The intermediate task used familiar variables that did not invoke underlying relations, whereas the abstract task contained unfamiliar variables that did not allow for inference of hypotheses about relations. Results showed that concrete participants performed more successfully and efficiently than intermediate participants, who in turn were equally successful and efficient as abstract participants. From these findings it was concluded that students learning by inquiry benefit little from knowledge of the meaning of variables per se. Some additional understanding of the way these variables are interrelated seems required to enhance inquiry learning processes and outcomes

Knowledge convergence in computer-supported collaborative learning

This study investigates how two types of graphical representation tools influence the way in which learners use knowledge resources in two different collaboration conditions. In addition, the study explores the extent to which learners share knowledge with respect to individual outcomes under these different conditions. The study also analyzes the relationship between the use of knowledge resources and different types of knowledge. The type of external representation (content-specific vs. content-independent) and the collaboration condition (videoconferencing vs. face-to-face) were varied. Sixty-four (64) university students participated in the study. Results showed that learning partners converged strongly with respect to their use of resources during the collaboration process. Convergence with respect to outcomes was rather low, but relatively higher for application-oriented knowledge than for factual knowledge. With content-specific external representation, learners used more appropriate knowledge resources without sharing more knowledge after collaboration. Learners in the computer-mediated collaboration used a wider range of resources. Moreover, in exploratory qualitative and quantitative analyses, the study found evidence for a relation between aspects of the collaborative process and knowledge convergence

Example-based learning: Integrating cognitive and social-cognitive research perspectives

Example-based learning has been studied from different perspectives. Cognitive research has mainly focused on worked examples, which typically provide students with a written worked-out didactical solution to a problem to study. Social-cognitive research has mostly focused on modeling examples, which provide students the opportunity to observe an adult or a peer model performing the task. The model can behave didactically or naturally, and the observation can take place face to face, on video, as a screen recording of the model's computer screen, or as an animation. This article reviews the contributions of the research on both types of example-based learning on questions such as why example-based learning is effective, for what kinds of tasks and learners it is effective, and how examples should be designed and delivered to students to optimize learning. This will show both the commonalities and the differences in research on example-based learning conducted from both perspectives and might inspire the identification of new research questions

Successfully carrying out complex learning tasks through guiding teams’ qualitative and quantitative reasoning

Slof, B., Erkens, G., Kirschner, P. A., Janssen, J., & Jaspers, J. G. M. (2012). Successfully carrying out complex learning tasks through guiding teams' qualitative and quantitative reasoning. Instructional Science, 40, 623-643. DOI: 10.1007/s11251-011-9185-2This study investigated whether and how scripting learners’ use of representational tools in a Computer Supported Collaborative Learning (CSCL)-environment fostered their collaborative performance on a complex business-economics task. Scripting the problem-solving process sequenced and made its phase-related part-task demands explicit, namely defining the problem and proposing multiple solutions, followed by determining suitability of the solutions and coming to a definitive problem solution. Two tools facilitated construction of causal or mathematical domain representations. Each was suited for carrying out the part-task demands of one specific problem-solving phase; the causal was matched to problem-solution phase and the mathematical (in the form of a simulation) to the solution-evaluation phase. Teams of learners (N = 34, Mean age = 15.7) in four experimental conditions carried out the part-tasks in a predefined order, but differed in the representational tool/tools they received during the collaborative problem-solving process. The tools were matched, partly matched or mismatched to the part-task demands. Teams in the causal-only (n = 9) and simulation-only (n = 9) conditions received either a causal or a simulation tool and were, thus, supported in only one of the two part-tasks. Teams in the simulation-causal condition (n = 9) received both tools, but in an order that was mismatched to the part-task demands. Teams in the causal-simulation condition (n = 7) received both tools in an order that matched the part-task demands of the problem phases. Results revealed that teams receiving part-task congruent tools constructed more task-appropriate representations and had more elaborated discussions about the domain. As a consequence, those teams performed better on the complex learning-task

Moving College Students to a Better Understanding of Substrate Specificity of Enzymes Through Utilizing Multimedia Pre-Training and an Interactive Enzyme Model

Scientists’ progress in understanding enzyme specificity uncovered a complex natural phenomenon. However, not all of the currently available biology textbooks seem to be up to date on this progress. Students’ understanding of how enzymes work is a core requirement in biochemistry and biology tertiary education. Nevertheless, current pre-college science education does not provide students with enough biochemical background to enable them to understand complex material such as this. To bridge this gap, a multimedia pre-training presentation was prepared to fuel the learner’s prior knowledge with discrete facts necessary to understand the presented concept. This treatment is also known to manage intrinsic cognitive load during the learning process. An interactive instructional enzyme model was also built to motivate students to learn about substrate specificity of enzymes. Upon testing the effect of this combined treatment on 111 college students, desirable learning outcomes were found in terms of cognitive load, motivation, and achievement. The multimedia pre-training group reported significantly less intrinsic cognitive load, higher motivation, and demonstrated higher transfer performance than the control and post-training groups. In this study, a statistical mediation model is also proposed to explain how cognitive load and motivation work in concert to foster learning from multimedia pre-training. This type of research goes beyond simple forms of “what works” to a deeper understanding of “how it works,” thus enabling informed decisions for multimedia instructional design. Multimedia learning plays multiple roles in science education. Therefore, science learners would be some of the first to benefit from improving multimedia instructional design. Accordingly, complex scientific phenomena can be introduced to college students in a motivating, informative, and cognitively efficient learning environment

Instructional Scaffolding in STEM Education: Strategies and Efficacy Evidence

science education; educational technology; learning and instructio

Learning to Diagnose with Simulations

This open access book presents 8 novel approaches to measure and improve diagnostic competences with simulation. The book compares the effects of interventions on these diagnostic competences in both teacher and medical education. It includes analyses showing that important aspects of diagnostic competences and effects of instructional interventions aiming to facilitate them are comparable for teachers and doctors. Through closely analyzing projects from medical education, mathematics education, biology education, and psychology, the reader is presented with multiple options for interventions that may be used in each of the subject areas and the improvements in diagnostic skills that could be expected from each simulation. The book concludes with an outline of promising future research on the use of simulations to facilitate professional competences in higher education in general, and for the advancement of diagnostic competencies in particular. This is an open access book

Exploring different instructional designs of a screen-captured video lesson: A mixed methods study of transfer of learnng

Digital instruction, whether in the form of training delivered on CD/DVD-ROMs or online courses delivered via the Internet is being used in all levels of education. It can, after all, increase student achievement if designed properly (Moersch, 1999). Many established instructional technologies (e.g. Microsoft PowerPoint®) have been researched to determine effective and ineffective instructional designs. However, newer technologies such as screen-captured videos, have not. Because the research of newer, multimedia instructional technology is \u27in its infancy\u27 (Mayer, 2001, p.194), a timely challenge for instructional technologists is to determine how to design and research these technologies. Theoretical frameworks on which to base these designs include Cognitive Load Theory (CLT) and the Cognitive Theory of Multimedia Learning (CTML). Each is based on Baddeley\u27s (1992) working memory model that says that our ability to think and process is constrained by working memory limitations. According to CLT, when learning new information, working memory can be overloaded by ineffectively designed instruction. One effective instructional design technique that can alleviate cognitive overload is the integration of scaffolds that serve as a bridge between what students know and what they have not yet learned. Similar to CLT, CTML also focuses on how to reduce cognitive load, only within a multimedia-based learning environment. An outcome of CTML is the segmenting effect, in which long periods of instruction are broken down into smaller sections in order to allow for better learning. Using these techniques, the researcher designed a mixed-methods study, which combined a 2x2 factorial-designed experiment with follow-up, qualitative interviews. Learning effects were tested with 108 participants at a Southeastern university who were given one of four different versions of screen-captured video lessons. Through the implementation of instructional techniques (scaffolding and segmentation) designed to decrease extraneous load, the researcher hoped but failed to promote long-term learning. Whereas an immediate test of learning transfer suggested that the effectiveness of the four instructional designs varied, the delayed measure of transfer indicated that those initial differences were fleeting. Several possibilities could explain this effect, including information overload and lack of motivation

Determinants of the control of dynamic systems: The role of structural knowledge

In educational and organisational settings it has become common practice to use computer-based complex problems that represent dynamic systems for assessment and training purposes. In the interpretation of performance scores and the design of training programs, it is often assumed that the capacity to effectively control the outcomes of a dynamic system depends on the acquisition of structural knowledge. Control performance scores are generally interpreted as evidence of individual differences in the capacity to acquire and utilise structural knowledge and training programs typically try to improve learners‘ mental models of the system of interest. However, a causal relationship between the acquisition of structural knowledge and successful system control has not been established, and some findings suggest that it may be possible to control dynamic systems in the absence of structural knowledge. Therefore, the goals of this project were to determine the conditions that are required to learn how to control dynamic systems and the psychological processes that separate successful from less successful problem solvers in the performance of this task. The main emphasis of this investigation was to clarify the role of structural knowledge in the control of dynamic systems and to identify sources of individual differences in problem solvers‘ capacity to acquire such knowledge and apply it in a goal-orientated application. In a series of studies, a combined experimental and differential approach was adopted to address these goals. This consisted of the experimental manipulation of the task and structural characteristics of complex problems combined with the use of process indicators and external psychometric tests. Study 1 examined whether problem solvers need to directly interact with a dynamic system in order to acquire structural knowledge that is useful for system control. Study 2 examined whether increments in structural knowledge lead to improvements in control performance and whether dynamic systems can be successfully controlled without structural knowledge. Study 3 examined whether the relationship between structural knowledge and control performance is moderated by system complexity. Each of these studies also investigated the role of fluid intelligence in the acquisition and application of knowledge. Additional methodological contributions include the application of Cognitive Load Theory to the design of the instructions used to manipulate structural knowledge, the use of randomly generated control performance scores to evaluate the success of performance and the development of a theoretically driven operationalisation of system complexity. Across the studies, it was found that structural knowledge was a necessary condition of better than random performance and that there was a causal relationship between structural knowledge and control performance. However, the likelihood that structural knowledge would be acquired and utilised was found to be dependent on the complexity of the system. Small increments in system complexity resulted in floor effects on performance. Fluid intelligence was found to play a crucial role in the acquisition and subsequent application of knowledge. Overall, the results indicate that the complexity of the system determines the amount of knowledge that is acquired by the problem solver, which in turn, combined with their intelligence, determines the quality of their control performance