United Brains for Complex Learning:A cognitive-load approach to collaborative learning efficiency

Kirschner, F. C. (2009). United Brains for Complex Learning. A cognitive-load approach to collaborative learning efficiency. Doctoral Dissertation, Open Universiteit, The Netherlands

Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency

Kirschner, F., Paas, F., & Kirschner, P. (2009). Individual and group-based learning from complex cognitive tasks: Effects on retention and transfer efficiency. Computers in Human Behavior, 25, 306-314.The effects of individual versus group learning (in triads) on efficiency of retention and transfer test performance in the domain of biology (heredity) among 70 high-school students were investigated. Applying cognitive load theory, the limitations of the working memory capacity at the individual level were considered an important reason to assign complex learning tasks to groups rather than to individuals. It was hypothesized that groups will have more processing capacity available for relating the information elements to each other and by doing so for constructing higher quality cognitive schemata than individuals if the high cognitive load imposed by complex learning tasks could be shared among group members. In contrast, it was expected that individuals who learn from carrying out the same complex tasks would need all available processing capacity for remembering the interrelated information elements, and, consequently, would not be able to allocate resources to working with them. This interaction hypothesis was confirmed by the data on efficiency of retention and transfer test performance; there was a favorable relationship between mental effort and retention test performance for the individual learners as opposed to a favorable relationship between transfer test performance and mental effort for the students who learned in groups

Superiority of collaborative learning with complex tasks: A research note on an alternative affective explanation

Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Superiority of collaborative learning with complex tasks: A research note on an alternative affective explanation. Computers in Human Behavior, 27(1), 53-57. doi:10.1016/j.chb.2010.05.012Kirschner, Paas, and Kirschner (2009c) used the theoretical framework of cognitive load to explain why the learning of a group of collaborating individuals was more efficient than that of individuals learning alone with high-complexity tasks but not with low-complexity tasks. The authors argued that collaboration circumvented the limitations of an individual’s working memory by creating an expanded cognitive capacity and by allowing for the distribution of cognitive load among group members. Inspired by research on efficacy, this study explored an alternative affective explanation of the results. By measuring the amount of mental effort learners expected to invest in working on a learning task before actually carrying out the task, this study showed that learners who had to collaboratively solve a high-complexity problem expected to invest less mental effort than learners who had to solve the problem alone. When confronted with low-complexity tasks, the expected amount of mental effort did not differ

Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect

This study investigated the differential effects of learning task complexity on both learning process and outcome efficiency of 83 individual and group learners in the domain of biology. Based upon cognitive load theory, it was expected that for high-complexity tasks, group members would learn in a more efficient way than individual learners, while for low-complexity tasks, individual learning would be more efficient. This interaction hypothesis was confirmed, supporting our premise that the learning efficiency of group members and individuals is determined by a trade-off between the group’s advantage of dividing information processing amongst the collective working memories of the group members and its disadvantage in terms of associated costs of information communication and action coordination

Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect

Kirschner, F., Paas, F., & Kirschner, P. A. (2011). Task complexity as a driver for collaborative learning efficiency: The collective working-memory effect. Applied Cognitive Psychology, 25, 615–624. doi: 10.1002/acp.1730.This study investigated the differential effects of learning task complexity on both learning process and outcome efficiency of 83 individual and group learners in the domain of biology. Based upon cognitive load theory, it was expected that for high-complexity tasks, group members would learn in a more efficient way than individual learners, while for low-complexity tasks, individual learning would be more efficient. This interaction hypothesis was confirmed, supporting our premise that the learning efficiency of group members and individuals is determined by a trade-off between the group’s advantage of dividing information processing amongst the collective working memories of the group members and its disadvantage in terms of associated costs of information communication and action coordination

Cognitive load theory and multimedia learning, task characteristics, and learning engagement: The current state of the art

Kirschner, F., Kester, L., & Corbalan, G. (2011). Cognitive load theory and multimedia learning, task characteristics, and learner engagement: The current state of the art. Computers in Human Behavior, 27, 1-4. doi:10.1016/j.chb.2010.05.003This special issue consists of 16 empirical papers, as well as a discussion based on the Third International Cognitive Load Theory Conference held at the Open Universiteit (Heerlen, The Netherlands) in 2009. All papers focus on improving instructional design from a cognitive load theory (CLT: Sweller, 1988; Sweller, Van Merriënboer, & Paas, 1998; Van Merriënboer & Sweller, 2005) perspective. They cover a wide variety of topics in which learner characteristics, tasks characteristics, and the interaction between both are studied in, new, innovative, but also traditional ways, thereby providing an overview of the current state of the art on CLT research. The overarching goal of all studies is to gain more understanding and insight into the optimal conditions under which learning can be successful, and students will be able to apply their acquired knowledge and skills in new or familiar problem solving situations. Together, the papers comprise three ways in which this overarching goal is reached: (1) by studying multimedia learning environments, (2) by studying different characteristics of a learning task and, (3) by studying how learners can be actively engaged in the learning process. Although, the research focus of most papers fit nicely within these research topics, some overlap is inevitable. The categorization has been made on the basis of the most prominent research focus and findings of each study

Experiences with Supporting Teachers with Scholarship of Teaching and Learning at a Research-Intensive University: Lessons Learned

Scholarship of Teaching and Learning (SoTL) is a fast-maturing field of study within many research-intensive universities. SoTL improves the quality of teaching, the professional development of teachers, and the recognition and appreciation of education. To encourage SoTL, it is important to know how to support teachers. This study describes two pilot initiatives with the goal to encourage and support teachers at a research-intensive university with their first SoTL project. In both pilots, a community of practice (CoP) approach was used. The experiences with the pilots were investigated with questionnaires and interviews. Based on the feedback of participants, albeit with some caution because of the relatively small sample size of this study, suggestions for future initiatives that support teachers new to SoTL at research-intensive institutes are: 1) the use of a combination of a CoP and individual guidance by experienced SoTL facilitators; 2) the creation of opportunities for formal and informal interaction to strengthen the CoP; 3) encouraging participants to work together on (shared) aligned projects; 4) the provision of structured course elements with guided discussions; 5) the provision of theoretical support regarding the principles of SoTL, for example, finding and interpreting literature, formulating a research question, and choosing the research methodology; 6) structure the inclusion of students’ participations, perspectives, and roles in SoTL; 7) some form of obligation, such as an official status of the initiative; and 8) institutional support, such as providing recognition, time, and financial support. The lessons learned in this study have relevance for all universities seeking to embrace, encourage, and support SoTL, especially for those initiating their first SoTL-supporting activities

Timing and Frequency of Mental Effort Measurement: Evidence in Favour of Repeated Measures

Van Gog, T., Kirschner, F., Kester, L., & Paas, F. (2012). Timing and frequency of mental effort measurement: Evidence in favor of repeated measures. Applied Cognitive Psychology, 26, 833-839. doi:10.1002/acp.2883Subjective mental effort rating scales are widely used in research on learning, instruction, and training. However, the timing and frequency of application of those rating scales differs between studies. Some apply a rating scale repeatedly after every task in a learning or test phase, others only once at the end of a phase. Four experiments are presented that investigated how timing and frequency of mental effort measurements affect the results obtained. The findings from Experiment 1 (between-subjects) and 2 (within-subjects), using different arrangements of simple and complex tasks, showed that a single rating after a series of tasks resulted in a higher mental effort score than the average of ratings provided immediately after every task. A similar result was obtained in Experiment 3 with series of complex tasks, but not with simple tasks. Experiment 4 showed that knowing beforehand that mental effort rating will be required after completing all tasks results in lower scores, but average retrospective ratings per task still differed from a single retrospective rating. Taken together, these experiments suggest that timing and frequency of effort ratings do affect the results obtained and that repeatedly measuring mental effort after each task in the series seems to be preferable.During the realization of this work, Tamara van Gog was supported by a Veni grant from the Netherlands Organization for Scientific Research (NWO; # 451-08-003)

Medical students' cognitive load in volumetric image interpretation:Insights from human-computer interaction and eye movements

Medical image interpretation is moving from using 2D- to volumetric images, thereby changing the cognitive and perceptual processes involved. This is expected to affect medical students' experienced cognitive load, while learning image interpretation skills. With two studies this explorative research investigated whether measures inherent to image interpretation, i.e. human-computer interaction and eye tracking, relate to cognitive load. Subsequently, it investigated effects of volumetric image interpretation on second-year medical students' cognitive load. Study 1 measured human-computer interactions of participants during two volumetric image interpretation tasks. Using structural equation modelling, the latent variable 'volumetric image information' was identified from the data, which significantly predicted self-reported mental effort as a measure of cognitive load. Study 2 measured participants' eye movements during multiple 2D and volumetric image interpretation tasks. Multilevel analysis showed that time to locate a relevant structure in an image was significantly related to pupil dilation, as a proxy for cognitive load. It is discussed how combining human-computer interaction and eye tracking allows for comprehensive measurement of cognitive load. Combining such measures in a single model would allow for disentangling unique sources of cognitive load, leading to recommendations for implementation of volumetric image interpretation in the medical education curriculum