John J. McKeown
Almost every aspect of modern life has some dimension associated with remote activity and high information velocity. Due to technological advances, education is more accessible now to more students than ever before. Many training and education programs are fully online or include a distance learning component. Responsibility for learning rests more squarely on the learner in this environment. This fact has prompted research in study methods and features of instructional design that help students monitor and control the learning process. If we, as learners, can recognize these attributes we might more fully engage them, enriching our learning experience. A process within which to frame this activity is self-regulated learning. It is facilitated by certain instructional practices like scaffolding, assessment, and feedback.
Self-regulated learning (SRL) as a strategy is commonly framed in terms of three concepts; metacognition, goal setting, and motivation. Zimmerman & Pons (1986) define SRL as actions directed at acquiring information or skill that involve agency, purpose, and instrumentality of self-perceptions by the learner. Metacognition supposes self-awareness of one’s learning which is developed through refection, articulation, and interaction. A demonstration assignment, for example, can facilitate metacognitive practices through the requirement to explain, measure, and validate tasks and processes. Goal setting prompts one to develop concepts and metrics for success and completeness with respect to knowledge, time, etc. While motivation for learning can be a function of many things, its durability is dependent upon the self-efficacy of the learner; one’s positive self-perception and confidence level.
Self-regulated learners will effectively articulate the metacognitive perceptions, implement a range of learning strategies, self-assess, and “pull” support from the system (McLoughlin and Marshall, 2000). Attributes that promote SRL include a high degree of learner control and autonomy; choices of form and activities. To facilitate SRL, system design must include easily navigable tools with prompt feedback. In addition, designers need to identify and understand the activities the users perform, their abilities, experiences, and limitations in perception, memory, learning, and attention (Wei-Chen & Chia-An, 2007, p.182).
Shih, Chen, Chang, & Kao (2010) propose a “state transition diagram” to monitor SRL as reflected in the actions of the learner in seven states: activity scheduling, schedule reviewing, synchronization, learning and monitoring, help seeking, learning evaluation, and analysis (p. 83). Each state contains methods and opportunities for learners to assess progress which may be tests or metrics. Scheduling by the student, for example, provides an estimate of when the learner expects to achieve certain objectives, which in turn can be assessed after the activity (this is not effective for novice learners who may not have the experience to be able to project a reasonable schedule).
Scaffolding is learning support through interaction with a more knowledgeable “other” which allows the learner to perform tasks more complex than they could accomplish alone and learn from that experience (Resiser & Tabak, 2014). The “other” may be a human instructor or a learning system, app, or software program. This support is embedded, situated in a context that is meaningful and relevant to the learner’s goals. As well, it provides a holistic approach that underscores the relationships and objectives of the learning domain. The application of scaffolding techniques can significantly improve the individual character of the experience, promote the transition to self-regulated learning, and mitigate the impediments to learning. Keys to achieving this are knowledge of the learner’s ability, establishment of shared goals, ongoing individual assessment, reflection on performance, and internalizing learning (Azevedo and Hadwin, 2005).
Attributes that support performance and learning include a risk-free environment, knowledge of errors and mistakes, instant access to supporting and corrective information, personalized guidance, and contextualized support interwoven throughout the experience. An environment is risk free in that mistakes will not result in damaged equipment, injury, etc. Knowledge of errors can be obtained through feedback mechanisms that highlight the mistake. Access to clarifying or corrective information can then be achieved through actuating traditional “help” features and definitions. Personalized guidance might result from diagnostic software and intelligent systems that can interpret learner actions. Contextualized support is in the form of relevant and immediately applicable information specific to the subject. Azevedo and Hadwin (2005) examined SRL and scaffold success noting that adaptive scaffolding helped increase learner’s declarative knowledge (i.e, the quality of dialogue where there was a shift from questions about task definition to strategy and goal setting) and SRL strategies.
The qualities of each of these components may differ depending on the experience of the learner. Supporting information for a novice may be pictures or animations while experts may access more abstract or symbolic representations. The supports are triggered by incidents where the learner violates training criteria or actively seeks guidance or clarification; that is, support is either “pushed” by the system or “pulled” by the learner (Clem, 2007).
Kursat (2006) offers a concept of scaffolding that consists of four components: conceptual, metacognitive, procedural, and strategic. Conceptual scaffolding assists the learner in making associations between ideas and construction of broader concepts. Advances in artificial intelligence and other innovations enhance the adaptive quality of scaffolds so as to diagnose and respond to individual learner progress. These responses might be hints, cuing, coaching, and advice. Metacognitive scaffolding focuses on and provides for reflection on the process of the learning experience such as an interactive forum in which the learner can express their perception of the processes and their own performance. Procedural scaffolding relates to the use of specific tools in the task execution like pre-emptive questioning, step-by-step instructions, and illustrations of next steps. Strategic scaffolding addresses how to identify the information that is needed to support learning, what new information should be introduced, and in what amount to extend the performance beyond what is possible if the learner had no support.
These results inform a system component that will govern which resources are available and their levels of complexity. Support may be definitional or dynamic and situation specific. In all cases, support should be calibrated for the existing knowledge of the learner and adaptive to individual progress. Scaffolding tools may be distributed throughout the learning environment (synergistically optimizing other scaffolding tools) or employed for a single objective (Resiser & Tabak, 2014).
Effectively reducing scaffolding support when appropriate, particularly those supports actuated by the learner, is a challenging problem. Metacognitive scaffolds require the learner to evaluate and self-monitor while intelligent tutoring agents systematically adapt to a learner’s progress (Azevedo & Hadwin, 2005). As with diagnosis of learning difficulty, system determination of when it is appropriate to start to pull away support may be further enhanced by technological advances. Using an expert system for determining rules, artificial intelligence utilizing sophisticated algorithms can interpret learner performance advances as a basis for limiting scaffolding support.
Assessment and Feedback
Sadler (1989) states that a goal of instructional systems is to assist learners in moving from feedback receivers to self-evaluators, making a connection with formative assessment. Formative assessments are those that involve individualized feedback, interaction, and qualitative evaluations of learner progress and products. Mao & Peck (2013) state that SRL and assessment are mutually beneficial; that assessment encourages planning and regulation of future SRL processes and learner autonomy (p.77).
Nicol and Macfarlane-Dick (2006) give us seven principles of feedback that promote SRL noting that it; clarifies what good performance is (i.e., exemplars and descriptions of assessment criteria), facilitates development of self-assessment (i.e., structured opportunities for reflection), delivers high quality information about student’s learning (i.e., connected to goals, timely, advisory, practical, prioritized, accessible), encourages dialogue (i.e., not one way transmission), encourages positive motivational beliefs and self-esteem (i.e., success depends on effort, not predetermined), provides opportunities to close the gap between current and desired performance (i.e., closing the feedback loop by re-working an assignment), provides information to instructors that help shape teaching (i.e., frequent diagnosis, easily interpretable assessments) (p. 205).
With an array of strategies come potential pitfalls. Among these is cognitive overload which may manifest itself differently depending upon the experience level of the learner (Nguyen, 2006). Information elements also need to relate to each other to optimally foster the creation of new schemas. The process of mental integration helps prevent the split-attention effect (Paas, Renkl, and Sweller, 2003). This occurs when learners need to shift back and forth between visual and audio channels. This increases extraneous cognitive load as the learner does so. Thus, information elements should be interwoven rather than sequential. Information discrimination and the fineness of its organization determines the complexity of schematic development, which is then more usefully employed for future learning.
Distance learning will undoubtedly continue to expand and develop. Recent events underscore its necessity. The proper use of support strategies can improve results and develop self-regulated learners. Students empowered with an understanding of these strategies can actively engage them and become masters of their own learning.
About the Author
John McKeown is prior service civil affairs with many years of experience in education administration and public finance. John has earned a BS, MS, CAS and completed PhD work in the learning sciences. He is currently an adjunct professor of finance at the University of Maryland Global Campus.
Zimmerman, B., & Pons, M. (1986). Development of a Structured Interview for Assessing Student Use of Self-Regulated Learning Strategies. American Educational Research Journal, 23(4), 614-628. Retrieved April 26, 2020, from www.jstor.org/stable/1163093
McLoughlin, C. and Marshall, L. (2000). Scaffolding: A model for learner support in an online teaching environment. In A. Herrmann and M.M. Kulski (Eds), Flexible Futures in Tertiary Teaching. Proceedings of the 9th Annual Teaching Learning Forum, 2-4 February 2000. Perth: Curtin University of Technology. http://cleo.murdoch.edu.au/confs/tlf/tlf2000/mcloughlin2.html
Wei-Chen, H., & Chia-An, C. (2007). Integrating advance organizers and multidimensional information display in electronic performance support systems. Innovations in Education and Teaching International, 44(2), 181-198. Retrieved from https://search.proquest.com/docview/210663186?accountid=37514
Shih, K.-P., Chen, H.-C., Chang, C.-Y., & Kao, T.-C. (2010). The Development and Implementation of Scaffolding-Based Self-Regulated Learning System for e/m-Learning. Educational Technology & Society, 13 (1), 80–93. http://go.galegroup.com.gate.lib.buffalo.edu/ps/i.do?p=AONE&sw=w&u=sunybuff_main&v=2.1&it=r&id=GALE%7CA221919030&sid=summon&asid=21b4b917c9dcdada6f772151d8bb432
Resiser, B. J., & Tabak, I. (2014). Scaffolding. In R. K. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences 2nd Ed. (pp. 44-62). New York: Cambridge University Press.
AZEVEDO, R., & HADWIN, A. (2005). Scaffolding self-regulated learning and metacognition – Implications for the design of computer-based scaffolds. Instructional Science, 33(5/6), 367-379. Retrieved from http://www.jstor.org.gate.lib.buffalo.edu/stable/41953688
Clem, J.D. (2007). The Synthetic Instructor: Implementation for Web-Based Electronic Performance Support Systems. Performance Improvement, 46(8), 27-31. Retrieved October 30, 2017 from https://www.learntechlib.org/p/77626/.
Kursat Cagiltay (2006) Scaffolding strategies in electronic performance support systems: types and challenges, Innovations in Education and Teaching International, 43:1, 93-103, DOI: 10.1080/14703290500467673
SADLER, D. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119-144. Retrieved from http://www.jstor.org.gate.lib.buffalo.edu/stable/23369143
Mao, J., & Peck, K. (2013). Assessment strategies, self-regulated learning skills, and perceptions of assessment in online learning. Quarterly Review of Distance Education, 14(2), 75+. Retrieved from http://go.galegroup.com.gate.lib.buffalo.edu/ps/i.do?p=AONE&sw=w&u=sunybuff_main&v=2.1&it=r&id=GALE%7CA369914304&sid=summon&asid=be99f9da881b7aca5f2e1c0e8c3704a9
Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199-218. doi:10.1080/03075070600572090
Nguyen, F. (2006), What you already know does matter: Expertise and electronic performance support systems. Perf. Improv., 45: 9–12. doi:10.1002/pfi.2006.4930450404
Fred Paas, Alexander Renkl, and John Sweller. Cognitive Load Theory and Instructional Design: Recent Developments. Educational Psychologist Vol. 38 , Iss. 1,2003