Why We Learn

Overview

Feedback and Self-Regulated Learning: A Theoretical Synthesis, written by Deborah L. Butler and Philip H. Winne in 1995, is a popular piece of academic literature. Google Scholar indicates that it has been cited by 1183 works. Its importance is likely a result of that way in which it views the nearly universally reported benefits of feedback through the prism of self-regulation. Essentially, the authors suggest a shift akin to the evolution from behaviorism to cognitivist ideas. Whereas the former focused exclusively on input and output, the latter posits that an idiosyncratic interpretation of stimuli must be considered. In this case, feedback serves as the stimuli to which the individual responds.

Feedback can be classified in a variety of ways; by timing (immediate or delayed), type (corrective, suggestive, elaborative), and by origin (external or internal). The authors also describe “cognitive feedback’’, relating perceived cues to use learning strategies and potential achievement. Five products of feedback are presented.

1. Confirm conceptual understanding
2. Add to conceptual understanding
3. Overwrite misunderstanding
4. “Tune’’ understanding
5. Restructure schemata (when entire conceptualization is wrong)

One of the primary points that the authors make is that all feedback is filtered through a wide array of preexisting learner qualities, e.g., domain specific history, goal orientation, epistemological beliefs, and efficacy. It is these qualities that lead to the afore mentioned “idiosyncratic interpretations’’ of feedback. So, while these may be the potential results of feedback, the way in which external feedback is filtered by the learner determines the magnitude (or existence) of the intended effect.

At the center of Butler and Winne’s argument is the idea of calibration, which refers to the monitoring of one’s ability to predict their own understanding. It is the degree to which feedback disconfirms one’s certitude in their ability to make such predictions that results in deep processing of feedback. More generally, this work suggests that one is more apt to work to resolve deficiencies in their ability to know themselves then to know an answer. In fact, the authors suggest that “calibration’’ is what’s monitored, rather than knowledge. According to Butler and Winne, the best feedback,

informs students about their monitoring of learning needs (achievement relative to goals in prior phases of engagement) and guides them in how to achieve learning objectives (cognitive engagement by applying tactics and strategies (p. 273).

So, feedback should be focused on helping students become calibrated, while simultaneously providing cues as to what types of learning strategies are likely to be most beneficial for specific learning tasks or domains. This differs from corrective feedback and questioning often employed when a student responds to a query incorrectly.

What This Means for Teachers

Feedback is effective. One need only review John Hattie’s table of effect sizes for support of its use in the classroom. However, this research points to the more complex nature of the interpretation of feedback by learners. Learners bring a set of preexisting beliefs and a history of experiences related to topics of study to the classroom. These inform they way in which students set and pursue goals. The combination of student history and goal orientations results in a unique interpretation of feedback. More importantly, internal feedback — also related to this personal history — in many ways supersedes external feedback’s ability to shape student performance.

Certainly, internal feedback alters external feedback’s effect. Awareness of this reality, and the idea that the primary goal of feedback should be to improve students’ internal “calibration’’ are the primary takeaways for the classroom instructor. Understanding each student’s history in the domain of study provides the foundation, while reflective writing tasks and short surveys might provide useful information as well. As with most tasks related to the interaction of humans, the interpretation of feedback is complex, and difficult (or at least time consuming) to do at the group level.

I’ve just received four questions that I must address as the primary component of comprehensive exams at Kent State University. I have eight weeks to answer these completely, after which they will be evaluated. If I’m successful, I will then be asked to defend my responses in person.

Research Methods in Educational Psychology and Instructional Technology

One of the things that may distinguish one discipline from another is the research that is done. The research may differ in the content, in the questions asked, and in the methods used. Instructional Technology and Educational Psychology are closely related disciplines in many ways, but they are also different. Choose a topic that is likely of interest to each field. Now design two studies, one that looks at the topic from an Educational Psychology perspective and one that does so from an Instructional Technology perspective. Explain how and why the two studies are different. What do these differences imply for the two disciplines?

Cognitive Load Theory in Designing Instruction

The basics of cognitive load theory seem well-established at this point, and it appears that instruction that fails to minimize extraneous cognitive load is likely to be less effective than instruction that takes this theory into account. First, explain the theory and its application clearly and succinctly. Then go beyond it. Specifically, it is unlikely to that cognitive load is the only factor that determines what and how much people learn from instruction, multimedia, and other elements. Describe another key factor that you believe, from your reading of the literature, has important effects on learning. Now consider how those two factors may interact in instructional settings. Describe a study that would illuminate these interactions and provide practical guidance in the design of instruction. You may narrow this down to a specific type of instruction, setting, population, or other factors.

The Application of Motivation Theory to Education

Discuss the current trends in contemporary motivation theory in the context of education. Compare and contrast motivation theories, such as need for achievement, attribution theory, achievement goal theory and theories of self-regulated learning. Make certain that your discussion focuses on theoretical developments that have stemmed from correcting earlier theoretical misconstructions. In your response be sure to cite research, which discusses each theoretical perspective’s take on achievement outcomes. Present some directions for the future related to the development of motivation theories and educational practices.

Instructional Design for Maximizing the Effectiveness of Technology

Many of us think that instructional design is one of the key foundations of instructional technology in general. In the current explosion of interest in and use of technology in education and training at all levels, it often appears that the focus is almost exclusively on the technology, with relatively little attention paid to instructional design and especially to the systematic processes of ID usually used within the field. There may be many reasons for that. Discuss the barriers that exist to applying instructional design procedures to improving the effectiveness of technology in schools, universities, or companies. Describe in some detail an approach to instructional design that would help teachers and others maximize the effectiveness of technology use in education. You may choose the setting, grade level, and other contextual factors that you concentrate on here. The approach may be one that is already in the ID literature or one that you have developed (with reference to the ID and related literatures).

Benjamin S. Bloom has two works from which to draw when interpreting the results of a new study by Carnegie Mellon. The key finding, related to the efficiency of this design, is summarized in the following quotation.

By combining the open-learning software with two weekly 50-minute class sessions in an intro-level statistics course, they found that they could get students to learn the same amount of material in half the time.

Essentially, the inclusion of this intelligent tutoring system allows the professor to discuss more nuanced and/or complex aspects of the content, and do so in one less class period. Bloom might infer that the observed improvements were due to an increase in the amount of time spent analyzing, synthesizing, and/or evaluating, i.e., at the higher levels of “his” taxonomy, while in class. Alternatively, he could point to his work on tutoring, and its corresponding “2 Sigma Problem”, which suggests that one can expect to observe a difference of +2 standard deviations when students work with a teacher in a one-to-one setting.

I’m inclined to agree with both of these hypothetical conclusions. I’m also reminded of Ewan McIntosh’s recent response to Will Richardson’s post commenting on an image of a lecture hall filled with (mostly Apple) computers. McIntosh’s critique, in he differentiates between curriculum and pedagogy, noting that teachers can control pedagogy but not curriculum, culminates with the following assertion.

The reason the picture presents a dubious message is that neither curriculum nor pedagogy have changed an iota in this learning space: it’s about the same layout – with as many apples on laps – as a Victorian classroom would have appeared.

I wonder how instructional design fits, as Carnegie Mellon’s design offloads the mundane, didactic portions of instruction to technological entities, thus freeing up space and time for the instructor/professor to do the sorts of things that are much harder for computers, even “intelligent” systems, to replicate. This is a good start, but can we go further?

The program’s efforts to maximize the contributions of technology are impressive. They’ve applied adaptive algorithms similar to those that are used in the GRE, which monitor and subsequently respond to students understanding. This is one of the first times this technology has been used outside of the assessment arena. (The article refers to its use as “novel”.) What needs to be considered, however, is a design that manufactures or generates time for face-to-face. That is to say that although the tutoring aspect of Bloom’s research is (partially) present in the form of the intelligent tutoring system, the potential of human tutoring is greater.

This is not the first time that I’ve considered using technology to offload the sort of lower level tasks of instruction. My presentation, entitled “A Shift in Focus: Designing for Face-to-Face” can be found here. Comments/critiques are welcome.

Photographers have a plethora of options when it comes to managing their digital assets. Off the top of my head I can name four rather conspicuous members of the group, Aperture, Lightroom, Photo Mechanic, and Portfolio. Each of these, in their own way, allows the photographer to quickly add metadata, resulting in quicker and more focused queries after storage. There are an entire libraries of books instructing photographers on the nuanced manner in which they should store, back-up, and generally manage their RAW files.

Software programmers are required to manage vast quantities of cryptic text. Additionally, they most often work on projects in groups, sharing code and a common vision for the way in which code is used, reused, and the way in which annotations (comments) are added to the code to ensure readability and consistency. The number of programming environments is also large. Apple’s XCode and the Eclipse platform are two very powerful solutions that are also free.

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If you’ve ever done any type of research, you are probably familiar with “keywords” or “subject terms” used by databases that index journal articles related to your field of interest. My understanding is that most journals allow authors to tag their articles with keywords of their own choosing. Let’s look at some examples from the submission guidelines for several of my favorite journals.

Educational Research

Articles should begin with a structured abstract and up to six keywords, and should not normally exceed 5,000 words.

Journal of Educational Psychology

All manuscripts must include an abstract containing a maximum of 180 words typed on a separate page. After the abstract, please supply up to five keywords or brief phrases. In fact, every journal listed on this page has the same requirements.

Educational Psychology Review

Directly below the abstract, provide 3–5 key words that express the manuscript’s precise content.

and, Teachers College Record does not reference, or I assume require, keywords.

I could go on and on. Zero specificity or guidance. Lists of suggested keywords are not mentioned. Now I know why I’ve so often seen the phrase “author supplied keywords”.

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