Recommended Reading Summary: A Chapter from “How People Learn: Brain, Mind, Experience, and School”

I recently posted some recommended reading that relates to a virtual class I recently taught on gamification.  (Here is the recording.)

This is my own summary of the first chapter on the list.  I highly recommend the entire book, which is available for free from the National Academies Press.  It was written in 2000 but it contains some great foundational information.

Chapter 1: “Learning: From Speculation to Science,” from How People Learn: Brain, Mind, Experience, and School, by Bransford, Brown, and Cocking.

The current methods we use to deliver learning have been shaped by research within the field of education, as well as related fields.  In recent decades, teachers and researchers have discovered approaches that assist the learner in understanding and retaining new information.  Learning professionals now design curricula from a perspective that is more focused on the learner’s needs.  Research related to child development, cognitive psychology, and neuroscience has molded the current approach to early education, and has influenced how emerging technology is incorporated into the learning experience.

In the past, there was less focus on the teaching of critical thinking skills, as well as the abilities to express concepts persuasively, and solve problems requiring complex thought.  Learning experiences were focused on developing basic literacy in fields such as reading and mathematics.  Today, humanity’s knowledge is increasing at a faster rate due to globalization and rapid development of technology.  It is still important that learners develop fundamental understanding of certain subjects, but that is not enough.  Learners must be taught to self-sustain, meaning they must learn on their own by asking meaningful questions.  Using new teaching methods will help instructors connect with those who were once considered “difficult” students.  New teaching methods will also provide a deeper knowledge of complex subjects to the majority of learners.

There has been extensive research regarding how to teach traditional subjects, such as writing skills, with a non-traditional approach.  These research efforts date back to the nineteenth century and have influenced a new school of behaviorism, which in turn led to changes in how psychological research is performed.

Learning is now thought of as a process to form connections between stimuli and responses.  For instance, hunger may drive an animal or person to learn the tasks or skills necessary to relieve hunger.  Even if complex trial and error is required to learn a skill, we will perform whatever process is necessary, as long as the reward we seek is desirable enough to warrant the effort.

Cognitive science approaches the study of learning in a multi-disciplinary fashion, incorporating research from many fields and using many tools and methodologies to further research.  Qualitative research methods complement and expand earlier experimental research efforts.  An important objective within this research is to better understand what it means to understand a topic.  Traditionally, the learner’s ability to memorize is assessed in order to determine competency.  While knowledge is necessary in order to solve problems, facts must be connected to each other in order for the learner to draw conclusions.  An organized framework of concepts and ideas will give the learner the context necessary to solve problems and establish long-term retention.

Our prior knowledge, skills, beliefs, and concepts influence how we organize and interpret new information.  We exist in an environment that consists of competing stimuli, and we must choose which stimuli to focus on based on what has been important or meaningful to us in the past.  Therefore, it’s important that our foundational knowledge be accurate.  Incomplete and inaccurate thinking needs to be challenged and corrected early so that the learner doesn’t build upon which is essentially a weak foundation of knowledge.  For example, it’s common to believe our personal experience of physical or biological phenomena represents a complete and correct knowledge of that phenomena, when in fact we need more information in order to understand what we’ve experienced.

It’s important that learners have some control over their learning process so they have the opportunity to gauge their own understanding of the topics being taught.  The ability to self-assess and reflect on areas of improvement leads to metacognition, which is the ability of a person to predict their own performance on various tasks and monitor current levels of mastery and understanding.  Learning can be reinforced through internal dialog, meaning a learner may choose to compare new information with old information, explain information to themselves, and look for areas where they fail to comprehend what has been taught.  Teaching a learner how to monitor their own learning is therefore a worthwhile investment in the building of deep knowledge.  An active learner is more able to transfer skills to new problems and challenges.

The difference between a novice and an expert within a subject matter is the depth of knowledge commanded by the expert.  Depth of knowledge allows a person to recognize patterns, relationships, and discrepancies that a less experienced or knowledgeable person might miss.  An expert has a better conceptual framework, and is able to better analyze what information they need to draw forward in their memory to solve a problem.  Understanding what information is relevant to a problem is key, because it allows a person to focus only on the information they need at that moment.  This makes the problem less complex.

In order to build understanding within a subject, a teacher may provide in-depth understanding of a few specific topics, rather than giving a superficial overview of many topics.  This allows learners to better digest defining concepts.  Assessments must reinforce this model by providing instructors with an understanding of the learner’s thought processes and testing in-depth, rather than superficial, knowledge.

Learners should be encouraged to reflect on what has been learned before going on to additional topics in order to support metacognition.  Teachers should be encouraged to consider the many tools and methodologies available to present new information, and select what is best for the learner and topic.  Building a community of learners who work together and accept failure will allow individuals to take risks and challenge themselves in the classroom.  There is no one “right” way to design a classroom environment – but there are ways that are more effective than others depending on the learner’s culture and expectations, and how competence is defined.

 

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Reading: TEL strategies from the perspective of disruptive innovation

This, from ALT Research in Learning Technology:

The publication of institutional strategies for learning, teaching and assessment in UK higher education is practically ubiquitous. Strategies for technology-enhanced learning are also widespread. This article examines 44 publically [sic] available UK university strategies for technology-enhanced learning, aiming to assess the extent to which institutional strategies engage with and accommodate innovation in technology-enhanced learning. … The article argues that sustaining innovation and efficiency innovation are more commonplace in the strategies than disruptive innovation, a position which is misaligned with the technology practices of students and lecturers.

After being called ‘disruptive’ before I was drawn to this paper as I don’t believe the disruption is in the traditional sense of someone sitting at the back of a classroom being a distraction or taking up too much time of others. No, this ‘disruption’ is more about the desire to think about the work, the technology, the learning, the students, etc. in a different way or from a different perspective. Once something is written in a policy or set of guidelines, it becomes the providence that is recommended and thus ‘normal’.

Being disruptive is, for me, just about understanding the policy or guidelines and thinking “Hmm, is this in our best interest? Is this still valid? Can we still innovate and improve our teaching, our students, our work?” This, from Flavin and Quintero‘s conclusion sums it up (emphasis my own) …

The examination of UK HEIs’ technology-enhanced learning strategies indicates a willingness to adapt on the part of universities but a disinclination to disrupt. Universities can describe themselves in their strategies as innovative yet, in practice, they are often ameliorative, more likely to pursue sustaining or efficiency than disruptive innovation.

Flavin, M. and Quintero, V. (2018). UK higher education institutions’ technology-enhanced learning strategies from the perspective of disruptive innovation. Research in Learning Technology, [online] 26(0). Available at: https://journal.alt.ac.uk/index.php/rlt/article/view/1987 [Accessed 2 May 2018].

Image source: Fio (CC BY NC-2.0)

Reading: Hashtags and retweets

I’m getting back into reading around things I enjoy and things that matter to me. What better place to start than with the archives of the RILT, the ALT Reasearch in Learning Technology open access journal.

Hashtags and retweets: using Twitter to aid Community, Communication and Casual (informal) learning, by Peter Reed.

Since the evolution of Web 2.0, or the Social Web, the way in which users interact with/on the Internet has seen a massive paradigm shift. Web 2.0 tools and technologies have completely changed the dynamics of the Internet, enabling users to create content; be it text, photographs or video; and furthermore share and collaborate across massive geographic boundaries. As part of this revolution, arguably the most significant tools have been those employing social media. This research project set out to investigate student’s attitudes, perceptions and activity toward the use of Twitter in supporting learning and teaching. In so doing, this paper touches on a number of current debates in higher education, such as the role (and perceived rise) of informal learning; and debates around Digital Natives/Immigrants vs. Digital Residents/Visitors. In presenting early research findings, the author considers the 3Cs of Twitter (T3c): Community, Communication and Casual (informal) learning. Data suggests that students cannot be classed as Digital Natives purely on age and suggests a rethinking of categorisations is necessary. Furthermore, the data suggests students are developing their own personal learning environments (PLEs) based on user choice. Those students who voluntarily engaged with Twitter during this study positively evaluated the tool for use within learning and teaching.

Reed, P. (2013). Hashtags and retweets: using Twitter to aid Community, Communication and Casual (informal) learning. Research in Learning Technology, 21. http://dx.doi.org/10.3402/rlt.v21i0.19692

Image source: Petit Louis (CC BY 2.0)

Adapting a MOOC for Research

Written by my colleague, Rachael Hodge, this article is a summary of our experience in identifying and developing research activities within the University of Warwick’s MOOC Literature and Mental Health.

The University of Warwick’s FutureLearn MOOC Literature and Mental Health: Reading for Wellbeing, which began its first presentation February 2016, was identified as an opportunity to conduct some research into the course subject area, ‘reading for wellbeing’ or ‘bibliotherapy’. Since 2013, a substantial body of literature has emerged in the field of MOOC-related research, with the MOOC becoming both the subject of and vehicle for research. The research approach adopted in Literature and Mental Health was influenced by other, recent research studies conducted within MOOCs, and particularly by the first presentation of Monash University’s Mindfulness for Wellbeing and Peak Performance FutureLearn MOOC, which distributed a stress survey to its learners in the first and final weeks of the course, to assess the efficacy of the course’s mindfulness practices. 

A number of reasons for trialling the use of this MOOC as a research tool were identified at the project’s outset. MOOCs give researchers access to large numbers of possible research participants, making MOOC research an attractive prospect, while the opportunity to gather valuable, potentially publishable data from free online courses may help to justify the time and resources expended during the production of new MOOCs. Several additional benefits of in-MOOC research were discovered during the process, including the potential for research activities to enrich the learner experience. However, a number of challenges and limitations were also encountered during the development of the study; the inevitable self-selection bias among MOOC learners, and the difficulty of establishing a control group within the MOOC activities, posed impediments to the gathering of useful, publishable data. 

Although we were aware of other MOOCs which had been used as vehicles for research, the process of adapting Literature and Mental Health for this research study was nonetheless an illuminating and instructive experience. The purpose of this paper is to reflect on that experience, and to consider the lessons learned during the process which may be useful in informing future research studies conducted via Massive Open Online Courses.

Reference
Hodge, R., (2016). Adapting a MOOC for Research: Lessons Learned from the First Presentation of Literature and Mental Health: Reading for Wellbeing. Journal of Interactive Media in Education. 2016(1), p.19. DOI:http://doi.org/10.5334/jime.428

Image source: Judy Dean (CC BY 2.0)

Research Trends in Massive Open Online Course (MOOC) Theses and Dissertations: Surfing the Tsunami Wave

Die Autoren haben 51 wissenschaftliche Abschlussarbeiten aus den Jahren 2008 - 2015 untersucht, die sich mit dem Thema MOOCs befasst haben. Gesucht wurden die Forschungstrends auf diesem noch jungen Gebiet. Dabei wurde festgestellt, dass die meisten Arbeiten aus dem Bildungsbereich kamen, es sich dabei vor allem um qualitative Studien (49 %) handelte und sich gerade in den letzten Jahren das Forschungsinteresse von den cMOOCs zu den xMOOCs bewegt hat. Aber die Autoren haben einleitend auch die MOOC-Entwicklung auf dem Gartner Hype Cycle abgebildet, was zu einer interessanten Darstellung und (die Autoren) zu dem Schluss führte: “MOOCs are at the verge of Plateau of Productivity which means that there will increasingly be a diversity in MOOC applications in the future.”
Aras Bozkurt, Nilgun Ozdamar Keskin und Inge de Waard, Open Praxis, Vol. 8, Issue 3, Juli - September 2016, S. 203-221 (via Academia.edu)

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Can Instructor Attractiveness lead to Higher Smile-Sheet Ratings? More Learning? A Research Brief.

In a recent research article, Tobias Wolbring and Patrick Riordan report the results of a study looking into the effects of instructor "beauty" on college course evaluations. What they found might surprise you -- or worry you -- depending on your views on vagaries of fairness in life.

Before I reveal the results, let me say that this is one study (two experiments), and that the findings were very weak in the sense that the effects were small.

Their first study used a large data set involving university students. Given that the data was previously collected through routine evaluation procedures, the researchers could not be sure of the quality of the actual teaching, nor the true "beauty" of the instructors (they had to rely on online images). 

The second study was a laboratory study where they could precisely vary the level of beauty of the instructor and their gender, while keeping the actual instructional materials consistent. Unfortunately, "the instruction" consisted of an 11-minute audio lecture taught by relatively young instructors (young adults), so it's not clear whether their results would generalize to more realistic instructional situations.

In both studies they relied on beauty as represented by facial beauty. While previous research shows that facial beauty is the primary way we rate each other on attractiveness, body beauty has also been found to have effects.

Their most compelling results:

1.

They found that ratings of attractiveness are very consistent across raters. People seem to know who is attractive and who is not. This confirms findings of many studies.

2.

Instructors who are more attractive, get better smile sheet ratings. Note that the effect was very small in both experiments. They confirmed what many other research studies have found, although their results were generally weaker than previous studies -- probably due to the better controls utilized.

3.

They found that instructors who are better looking engender less absenteeism. That is, students were more likely to show up for class when their instructor was attractive.

4.

They found that it did not make a difference on the genders of the raters or instructors. It was hypothesized that female raters might respond differently to male and female instructors, and males would do the same. But this was not found. In previous studies there have been mixed results.

5.

In the second experiment, where they actually gave learners a test of what they'd learned, attractive instructors engendered higher scores on a difficult test, but not an easy test. The researchers hypothesize that learners engage more fully when their instructors are attractive.

6.

In the second experiment, they asked learners to either: (a) take a test first and then evaluate the course, or (b) do the evaluation first and then take the test. Did it matter? Yes! The researchers hypothesized that highly-attractive instructors would be penalized for giving a hard test more than their unattractive colleagues. This prediction was confirmed. When the difficult test came before the evaluation, better looking instructors were rated more poorly than less attractive instructors. Not much difference was found for the easy test.

Ramifications for Learning Professionals

First, let me caveat these thoughts with the reminder that this is just one study! Second, the study's effects were relatively weak. Third, their results -- even if valid -- might not be relevant to your learners, your instructors, your organization, your situation, et cetera!

  1. If you're a trainer, instructor, teacher, professor -- get beautiful! Obviously, you can't change your bone structure or symmetry, but you can do some things to make yourself more attractive. I drink raw spinach smoothies and climb telephone poles with my bare hands to strengthen my shoulders and give me that upside-down triangle attractiveness, while wearing the most expensive suits I can afford -- $199 at Men's Warehouse; all with the purpose of pushing myself above the threshold of ... I can't even say the word. You'll have to find what works for you.
  2. If you refuse to sell your soul or put in time at the gym, you can always become a behind-the-scenes instructional designer or a research translator. As Clint said, "A man's got to know his limitations."
  3. Okay, I'll be serious. We shouldn't discount attractiveness entirely. It may make a small difference. On the other hand, we have more important, more leverageable actions we can take. I like the research-based findings that we all get judged primarily on two dimensions warmth/trust and competence. Be personable, authentically trustworthy, and work hard to do good work.
  4. The finding from the second experiment that better looking instructors might prompt more engagement and more learning -- that I find intriguing. It may suggest, more generally, that the likability/attractiveness of our instructors or elearning narrators may be important in keeping our learners engaged. The research isn't a slam dunk, but it may be suggestive.
  5. In terms of learning measurement, the results may suggest that evaluations come before difficult performance tests. I don't know though how this relates to adults in workplace learning. They might be more thankful for instructional rigor if it helps them perform better in their jobs.
  6. More research is needed!

Research Reviewed

Wolbring, T., & Riordan, P. (2016). How beauty works. Theoretical mechanisms and two
empirical applications on students' evaluation of teaching. Social Science Research, 57, 253-272.

Practice Firms — Giving People Real-World Experience

Today's New York Times has a fascinating article on the mostly European concept of practice firms. As the name implies, practice firms give people practice in doing work.

This seems to align well with the research on learning that suggests that learning in a realistic context, getting lots of retrieval practice and feedback, and many repetitions spaced over time can be the most effective way to learn. Of course, the context and practice and feedback have to be well-designed and aligned with the future work of the learner.

Interestingly, there is an organization that is solely devoted to the concept. EUROPEN-PEN International is the worldwide practice enterprise network. The network consists of over 7,500 Practice Enterprises in more than 40 countries. It has a FaceBook page and a website.

I did a quick search to see if there was an scientific research on the use of practice firms, but I didn't uncover anything definitive...If you know of scientific research, or other rigorous evidence, let me know...

 

 

Research on Mathematics Education for U.S. First Graders

A recent research review (by Paul L. Morgan, George Farkas, and Steve Maczuga) finds that teacher-directed mathematics instruction in first grade is superior to other methods for students with "math difficulties." Specifically, routine practice and drill was more effective than the use of manipulatives, calculators, music, or movement for students with math difficulties.

For students without math difficulties, teacher-directed and student-centered approaches performed about the same.

In the words of the researchers:

In sum, teacher-directed activities were associated with greater achievement by both MD and non-MD students, and student-centered activities were associated with greater achievement only by non-MD students. Activities emphasizing manipulatives/calculators or movement/music to learn mathematics had no observed positive association with mathematics achievement.

For students without MD, more frequent use of either teacher-directed or student-centered instructional practices was associated with achievement gains. In contrast, more frequent use of manipulatives/calculator or movement/music activities was not associated with significant gains for any of the groups.

Interestingly, classes with higher proportions of students with math difficulties were actually less likely to be taught with teacher-directed methods -- the very methods that would be most helpful!

 

Will's Reflection (for both Education and Training)

These findings fit in with a substantial body of research that shows that learners who are novices in a topic area will benefit most from highly-directed instructional activities. They will NOT benefit from discovery learning, problem-based learning, and similar non-directive learning events.

See for example:

  • Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching. Educational Psychologist, 41(2), 75-86.
  • Mayer, R. E. (2004). Should There Be a Three-Strikes Rule Against Pure Discovery Learning? American Psychologist, 59(1), 14-19.

As a research translator, I look for ways to make complicated research findings usable for practitioners. One model that seems to be helpful is to divide learning activities into two phases:

  1. Early in Learning (When learners are new to a topic, or the topic is very complex)
    The goal here is to help the learners UNDERSTAND the content. Here we provide lots of learning support, including repetitions, useful metaphors, worked examples, immediate feedback.
  2. Later in Learning (When learners are experienced with a topic, or when the topic is simple)
    The goal here is to help the learners REMEMBER the content or DEEPEN they're learning. To support remembering, we provide lots of retrieval practice, preferably set in realistic situations the learners will likely encounter -- where they can use what they learned. We provide delayed feedback. We space repetitions over time, varying the background context while keeping the learning nugget the same. To deepen learning, we engage contingencies, we enable learners to explore the topic space on their own, we add additional knowledge.

What Elementary Mathematics Teachers Should Stop Doing

Elementary-school teachers should stop assuming that drill-and-practice is counterproductive. They should create lesson plans that guide their learners in understanding the concepts to be learned. They should limit the use of manipulatives, calculators, music, and movement. Ideas about "arts integration" should be pushed to the back burner. This doesn't mean that teachers should NEVER use these other methods, but they should be used to create occasional, short, and rare moments of variety. Spending hours using manipulatives, for example, is certainly harmful in comparison with more teacher-directed activities.

 

Training Maximizers

A few years ago, I created a simple model for training effectiveness based on the scientific research on learning in conjunction with some practical considerations (to make the model's recommendations leverageable for learning professionals). People keep asking me about the model, so I'm going to briefly describe it here. If you want to look at my original YouTube video about the model -- which goes into more depth -- you can view that here. You can also see me in my bald phase.

The Training Maximizers Model includes 7 requirements for ensuring our training or teaching will achieve maximum results.

  • A. Valid Credible Content
  • B. Engaging Learning Events
  • C. Support for Basic Understanding
  • D. Support for Decision-Making Competence
  • E. Support for Long-Term Remembering
  • F. Support for Application of Learning
  • G. Support for Perseverance in Learning

Here's a graphic depiction:

 Training Maximizers Willversion with Copyright

Most training today is pretty good at A, B, and C but fails to provide the other supports that learning requires. This is a MAJOR PROBLEM because learners who can't make decisions (D), learners who can't remember what they've learned (E), learners who can't apply what they've learned (F), and learners who can't persevere in their own learning (G); are learners who simply haven't received leverageable benefits.

When we train or teach only to A, B, and C, we aren't really helping our learners, we aren't providing a return on the learning investments, we haven't done enough to support our learners' future performance.