e-Learning Ecologies MOOC’s Updates

Achieving self-efficacy through peer modeling and differentiated instruction

The idea that every classroom would include a differentiated group of learners that might require individualized ways of achieving learning outcomes seems so blatantly obvious, yet this obvious wisdom rarely informs classroom instruction on a regular basis.

source: istockphoto.com/yvdavyd

Educators put the blame on demanding standardization, large classroom size, and lack of time as the most common culprits for the underdeveloped personalization of learning. How can we recognize the differences between learners, and how can we address them in creating a more effective, learning-conducive, level playing field? While it would be impossible (and impractical) to deal with every student individually, we can start with just three major factors, following the BBC Active page that describes differentiation methods. These are:

  • readiness to learn,
  • learning needs, and
  • interest.

The Edutopia community offers 18 teacher-tested tips and strategies for differentiated instruction, as well as a list of resources for every step of the way in understanding, planning, and implementing differentiated pedagogy.

One of the ways of tackling the crippling shortage of time in the classroom – without sacrificing high standards of achievement and without compromising individual student needs – is through distribution of responsibilities and cognition across an extended community of learners (including machines that assist these processes). In terms of assessment, this kind of distribution makes for a measurably more effective, reliable, transparent, and democratic system of constructive feedback on the process of learning (Cope & Kalantzis, 2015, p. 380). Even more importantly, it allows for a more conscious inclusion of social aspects of learning. For learners, this translates into a more participatory and active approach that cultivates learner autonomy, builds up individual confidence and self-reliance through collaboration and peer modeling, instead of passive didacticism.

Albert Bandura’s oft-quoted theoretical contributions to our understanding of the complexities of learning are particularly relevant to this discussion, as they tie together so many factors that make successful learning possible – if by ‘success’ we mean lifelong learning, not just strictly academic achievement. Bandura clearly shows that human actions cannot be understood without taking into consideration the social systems in which they take place. In order to affect change in human behaviors and actions (and learning is change), we must account for the forces that shape our individual choices, desire to learn, and willingness to overcome obstacles (what can be broadly called motivation). Bandura builds off his own ideas delineated in the seminal 1986 book as a theory that “posits a multifaceted causal structure that addresses both the development of competencies and the regulation of action” in a later article that focuses on the role of agency in learning. Crucially, Bandura recognizes the importance of forming appropriate self-image in the minds of learners. He reminds us that

“Knowledge structures representing the models, rules and strategies of effective action serve as cognitive guides for the construction of complex patterns of behavior. These knowledge structures are formed from the styles of thinking and behavior that are modeled, from the outcomes of exploratory activities, verbal instruction, and innovative cognitive syntheses of acquired knowledge” (1999, 24).

Positive models are the basis of self-efficacy, which should be the ultimate goal of learning. Such models can be successfully shaped by fellow learners, and research has consistently confirmed the role they play in increasing self-efficacy (Bandura 1978; Brown and Inouye 1978; Rosenthal and Bandura 1978). For example, Pajares and Miller (1994) demonstrate the positive correlation between self-concept beliefs (judgements about perceived competence) and increased ability to solve mathematical problems. Exposing peers to models of success, as they found, “may not necessarily have demoralizing effects on observers. Indeed, observing a model of comparable ability achieve success would create success expectations in observers and thus enhance their task motivation. Conversely, seeing someone comparable to oneself repeatedly fail at a task would create failure expectations in observers and result in lowered persistence and effort” (901). And this is exactly what Brown and Inouye (1978) have found in their study, which followed a group of math learners whose perceived low competence and a sense of helplessness were likewise modeled by their peers.

An interesting twist on this discussion in the form of a caveat is offered in Robert Daniel’s post Groupthink, Excessive Conformity, In-Group Bias, etc. (down sides of "collaborative intelligence" right here in Scholar.

References:

Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ: Prentice Hall.

Bandura, A. (1999). Social cognitive theory: An agentic perspective. Asian Journal of Social Psychology 2(1), pp. 21-41.

Brown, I. & Inouye, D. K. (1978). Learned helplessness through modeling: The role of perceived similarity in competence. Journal of Personality and Social Psychology, 36(8), pp. 900-908.

Cope, B. & Kalantzis, M. Learning and new media. (2015). In D. Scott & E. Hargreaves (Eds.), The SAGE handbook of learning (pp. 374-88). SAGE Publications. DOI: http://dx.doi.org/10.4135/9781473915213.n35

Pajares, F. and Miller, M. D. (1994). Role of self-efficacy and self-concept beliefs in mathematical problem solving: A path analysis. Journal of Educational Psychology 86(2), pp. 193–203. DOI: 10.1037/0022-0663.86.2.193.