Experimental research around virtual humans acting as pedagogical agents has often taken place in learner-paced learning environments. However, virtual humans are increasingly embedded in educational materials such as instructional videos, where the pacing of the environment can be fundamentally different than a stand-alone learner-controlled software package. This study examined the influence of three types of pacing with varying levels of learner control when learning from an instructional video with an embedded virtual human. The results of our three-group randomized study indicate that increased learner control led to the strongest learning outcomes, although moderate learner control was the most instructionally efficient. The results suggest that some aspects of learner control can be beneficial when learning from instructional videos with embedded virtual humans.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
Tax calculation will be finalised during checkout.
Mayer (2014) describes the terms essential processing, extraneous processing, and generative processing as being analogous with the terms intrinsic cognitive load, extraneous cognitive load, and germane cognitive load, respectively. This being the case, we use the terminology of cognitive load theory to create a consistency between the theoretical framework and the measurement approach used in this study.
Ayres, P. (2018). Subjective measures of cognitive load: What can they reliably measure? In R. Z. Zheng (Ed.), Cognitive load measurement and application: A theoretical framework for meaningful research and practice (pp. 9–28). New York, NY: Routledge.
Carolan, T. F., Hutchins, S. D., Wickens, C. D., & Cumming, J. M. (2014). Costs and benefits of more learner freedom: Meta-analyses of exploratory and learner control training methods. Human Factors, 56(5), 999–1014.
Chi, M. T., Kang, S., & Yaghmourian, D. L. (2017). Why students learn more from dialogue-than monologue-videos: Analyses of peer interactions. Journal of the Learning Sciences, 26, 10–50.
Chi, M. T., Roy, M., & Hausmann, R. G. (2008). Observing tutorial dialogues collaboratively: Insights about human tutoring effectiveness from vicarious learning. Cognitive Science, 32(2), 301–341.
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
Craig, S. D., Chi, M. T. H., & VanLehn, K. (2009). Improving classroom learning by collaboratively observing human tutoring videos while problem solving. Journal of Educational Psychology, 101, 779–789.
Craig, S. D., Gholson, B., Brittingham, J. K., Williams, J., & Shubeck, K. T. (2012). Promoting vicarious learning of physics using deep questions with explanations. Computers & Education, 58, 1042–1048.
Craig, S. D., Gholson, B., & Driscoll, D. (2002). Animated pedagogical agents in multimedia educational environments: Effects of agent properties, picture features, and redundancy. Journal of Educational Psychology, 94, 428–434.
Craig, S. D., & Schroeder, N. L. (2018). Design principles for virtual humans in educational technology environments. In K. Millis, D. Long, J. Magliano, & K. Wiemer (Eds.), Deep learning: Multi-disciplinary approaches (pp. 128–139). New York, NY: Routledge.
Craig, S. D., Sullins, J., Witherspoon, A., & Gholson, B. (2006). Deep-level reasoning questions effect: The role of dialog and deep-level reasoning questions during vicarious learning. Cognition and Instruction, 24(4), 565–591.
de Bruin, A. B., & van Merriënboer, J. J. (2017). Bridging cognitive load and self-regulated learning research: A complementary approach to contemporary issues in educational research. Learning and Instruction, 51, 1–9.
Doolittle, P. E., Bryant, L. H., & Chittum, J. R. (2015). Effects of degree of segmentation and learner disposition on multimedia learning. British Journal of Educational Technology, 46(6), 1333–1343.
Field, A. (2013). Discovering statistics using IBM SPSS statistics. Thousand Oaks, CA: Sage Publications.
Gay, G. (1986). Interaction of learner control and prior understanding in computer-assisted video instruction. Journal of Educational Psychology, 78(3), 225.
Gholson, B., & Craig, S. D. (2006). Promoting constructive activities that support vicarious learning during computer-based instruction. Educational Psychology Review, 18, 119–139.
Graesser, A. C., Cai, Z., Morgan, B., & Wang, L. (2017). Assessment with computer agents that engage in conversational dialogues and trialogues with learners. Computers in Human Behavior. https://doi.org/10.1016/j.chb.2017.03.041.
Hannafin, M. J., & Colamaio, M. A. E. (1987). The effects of variations in lesson control and practice on learning from interactive video. ECTJ, 35(4), 203–212.
Heidig, S., & Clarebout, G. (2011). Do pedagogical agents make a difference to student motivation and learning? Educational Research Review, 6(1), 27–54.
Herreid, C. F., & Schiller, N. A. (2013). Case studies and the flipped classroom. Journal of College Science Teaching, 42(5), 62–66.
Johnson, W. L., & Lester, J. C. (2016). Face-to-face interaction with pedagogical agents, twenty years later. International Journal of Artificial Intelligence in Education, 26(1), 25–36.
Karich, A. C., Burns, M. K., & Maki, K. (2014). Updated meta-analysis of learner control within educational technology. Review of Educational Research, 84(3), 392–410.
Lawless, K. A., & Brown, S. W. (1997). Multimedia learning environments: Issues of learner control and navigation. Instructional Science, 25(2), 117–131.
Mayer, R. E. (2014). Cognitive theory of multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 43–71). New York, NY: Cambridge University Press.
Mayer, R. E. (2017). Using multimedia for e-learning. Journal of Computer Assisted learning, 33, 403–423.
Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user interaction foster deeper understanding of multimedia messages? Journal of Educational Psychology, 93(2), 390–397.
Mayer, R. E., Dow, G. T., & Mayer, S. (2003). Multimedia learning in an interactive self-explaining environment: What works in the design of agent-based microworlds? Journal of Educational Psychology, 95(4), 806–813.
Mayer, R. E., & Moreno, R. (1998). A split-attention effect in multimedia learning: Evidence for dual processing systems in working memory. Journal of Educational Psychology, 90(2), 312–320.
Mayer, R. E., & Pilegard, C. (2014). Principles for managing essential processing in multimedia learning: Segmenting, pre-training and modality principles. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 316–344). New York, NY: Cambridge University Press.
McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22, 276–282.
Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Belmont: Cengage Learning.
Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91(2), 358–368.
Moreno, R., & Mayer, R. E. (2005). Role of guidance, reflection, and interactivity in an agent-based multimedia game. Journal of Educational Psychology, 97(1), 117–128.
Moreno, R., Mayer, R. E., Spires, H. A., & Lester, J. C. (2001). The case for social agency in computer-based teaching: Do students learn more deeply when they interact with animated pedagogical agents? Cognition and Instruction, 19(2), 177–213.
Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84, 429–434.
Paas, F., & Sweller, J. (2014). Implications of cognitive load theory for multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 27–42). New York, NY: Cambridge University Press.
Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38(1), 63–71.
Paas, F., & van Merriënboer, J. J. G. (1993). The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human Factors, 35(4), 737–743.
Paas, F. G. W. C., van Merriënboer, J. J. G., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and Motor Skills, 79(1), 419–430.
Scheiter, K. (2014). The learner control principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 487–512). New York, NY: Cambridge University Press.
Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19, 285–307.
Schroeder, N. L., & Adesope, O. O. (2013). How does a contextually-relevant peer pedagogical agent in a learner-attenuated system-paced learning environment affect cognitive and affective outcomes? Journal of Teaching and Learning with Technology, 2(2), 114–133.
Schroeder, N. L., & Adesope, O. O. (2015). Impacts of pedagogical agent gender in an accessible learning environment. Educational Technology & Society, 18(4), 401–411.
Schroeder, N. L., Adesope, O. O., & Gilbert, R. B. (2013). How effective are pedagogical agents for learning? A meta-analytic review. Journal of Educational Computing Research, 49(1), 1–39.
Schroeder, N. L., & Craig, S. D. (2017). The effect of pacing on learners’ perceptions of pedagogical agents. Journal of Educational Computing Research, 55(7), 937–950. https://doi.org/10.1177/0735633116689790.
Sweller, J. (2005). Implications of cognitive load theory for multimedia learning. In R. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 19–30). New York, NY: Cambridge University Press.
Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123–138.
Sweller, J., van Merriënboer, J. J., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31, 261–292. https://doi.org/10.1007/s10648-019-09465-5.
Szpunar, K. K., Jing, H. G., & Schacter, D. L. (2014). Overcoming overconfidence in learning from video-recorded lectures: Implications of interpolated testing for online education. Journal of Applied Research in Memory and Cognition, 3(3), 161–164.
Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In D. H. Schunk & B. Zimmerman (Eds.), Handbook of self-regulation of learning and performance (pp. 15–32). New York, NY: Routledge.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Schroeder, N.L., Chin, J. & Craig, S.D. Learner Control Aids Learning from Instructional Videos with a Virtual Human. Tech Know Learn 25, 733–751 (2020). https://doi.org/10.1007/s10758-019-09417-6
- Pedagogical agent
- Virtual human
- Learner control
- Instructional video