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Top Ten

What Kinds of Practice and Feedback Enhance Learning?

  1. Mismatched expectations can be difficult to diagnose and waste much time.
  2. In the absence of structure, learners tend to glide along more comfortable paths (i.e., making slides prettier rather than more content-rich).
  3. Deliberate practice without effective feedback can instill new unknown bad habits.
  4. Learning and performance are best fostered when students engage in practice that:
    1. focuses on a specific goal or criterion for performance,
    2. targets an appropriate level of challenge relative to students’ current performance, and
    3. is of sufficient quantity and frequency to meet the performance criteria.
  5. Articulate goals in measurable ways:
    1. Use good metrics that relate to the objective and to possible performance from the learner.
    2. Include higher-level goals.
  6. Concurrent learning can work, but often not at the novice skill level.
  7. While quality of practice matters, time on task is also important.
  8. Practice tends to be most effective at improving skills in the "competent" range. (Novices grapple with known knowns, competent practitioners with known unknowns, and experts with unknown unknowns.)
  9. Instructors should point out progress as it is made so that students recognize their accomplishment and discern the change in their behavior, especially when gradual.
  10. Grades and scores provide some information on the degree to which students' performance has met the criteria, they do not explain which aspects did or did not meet the criteria and how, so more specific feedback is necessary.

###How Learning Works, Chapter 1.

###How does students’ prior knowledge affect their learning?

  1. Learners come with past experiences and models of knowledge. If we can activate that prior knowledge and correctly link it to what we are trying to teach, the effect will be increased retention and a greater ability to apply what we are teaching to novel problems.
  2. If the learner's past knowledge is not activated, we lose this integration of knowledge and amplifying effect of their past experience, declarative and procedural knowledge.
  3. If the learner comes with incorrect information or misunderstands how the new knowledge relates to their past experience, their learning can be hindered until they understand the misconception.
  4. The nature of misconceptions is that the learner will not realise they have them. Specifically, if asked they may well report that they understand the situation.
  5. Testing knowledge (diagnostic assessment), with well designed multiple choice questions for example, will reveal the misconceptions and form a basis for correcting them.
  6. Well crafted challenges will provide the learner with information about their understanding. Faded examples support the student and provide a “win” at the start and indicate a lack when they stop being able to complete the challenge.
  7. Successfully completing a challenge while still holding a misconception about the subject of the challenge is a very, very bad thing…… It increases the learners confidence in their incorrect model.
  8. Using examples that involve universal activities rather than domain specific or highly technical examples will maximise the number of correct connections that form the basis of transferring knowledge. It is a delicate balance because if it is too trivial or simple, students may dismiss it as not important or that they already understand the information and switch off to conserve energy. Engagement/entertaining examples, maybe with some humour or an interesting story will allow you to keep engagement while speaking directly to most people’s experiences.
  9. Analogies are useful in connecting past understanding to a current problem but be explicit about how it applies to the situation because the learner may not understand where the analogy breaks down or stops being applicable.
  10. Point number 10 is a hidden point, it can be found by carefully reading the book! :-)

Books

Susan Ambrose et al: How Learning Works: Seven Research-Based Principles for Smart Teaching. : An excellent overview of what we know about education and why we believe it's true, covering everything from cognitive psychology to social factors.

Elizabeth Green: Building a Better Teacher. : A well-written look at why educational reforms in the past 50 years have mostly missed the mark, and what we should be doing instead.

Mark Guzdial: Learner-Centered Design of Computing Education: Research on Computing for Everyone. : A well-researched investigation of what it means to design computing courses for everyone, not just people who are going to become professional programmers, from one of the leading researchers in CS education.

Doug Lemov: Teach Like a Champion 2.0. : Presents 62 classroom techniques drawn from intensive study of thousands of hours of video of good teachers in action.

Therese Huston: Teaching What You Don't Know. : A pointed, funny, and very useful book that explores exactly what the title suggests.

Jane Margolis and Allan Fisher: Unlocking the Clubhouse: Women in Computing. : A groundbreaking report on the gender imbalance in computing, and the steps Carnegie-Mellon took to address the problem.

Claude M. Steele: Whistling Vivaldi: How Stereotypes Affect Us and What We Can Do. : Explains and explores stereotype threat and strategies for addressing it.

Dani Byrd and Toben H. Mintz: Discovering Speech, Words, and Mind. : Discusses the neuroscience and psychology of the distinction between phonics and whole-language approaches to reading instruction.

Papers

Baume: "[Writing and Using Good Learning Outcomes]({{ site.root }}/files/papers/baume-learning-outcomes-2009.pdf)" : A useful detailed guide to constructing useful learning outcomes.

Borrego and Henderson: "[Increasing the Use of Evidence-Based Teaching in STEM Higher Education: A Comparison of Eight Change Strategies]({{ site.root }}/files/papers/borrego-henderson-change-strategies-2014.pdf)" : Describes eight approaches to effecting change in STEM education that form a useful framework for thinking about how Software Carpentry and Data Carpentry can change the world.

Brown and Altadmri: "[Investigating Novice Programming Mistakes: Educator Beliefs vs Student Data]({{ site.root }}/files/papers/brown-educator-vs-learner-beliefs-2014.pdf)" : Compares teachers' opinions about common programming errors with data from over 100,000 students, and finds only weak consensus amongst teachers and between teachers and data.

Crouch and Mazur: "[Peer Instruction: Ten Years of Experience and Results]({{ site.root }}/files/papers/crouch-mazur-peer-instruction-ten-years-2001.pdf)" : An early report on peer instruction and its effects in the classroom.

Deans for Impact: "[The Science of Learning]({{ site.root }}/files/papers/science-of-learning-2015.pdf)" : Summarizes cognitive science research related to how students learn, and connects it to practical implications for teaching and learning.

Guzdial: "[Exploring Hypotheses about Media Computation]({{ site.root }}/files/papers/guzdial-mediacomp-retrospective-2013.pdf)" : A look back on 10 years of media computation research.

De Bruyckere et al: "[Urban Myths About Learning and Education]({{ site.root }}/files/papers/de-bruyckere-urban-myths-2015.pdf)" : A one-page summary drawn from their book of the same name.

Guzdial: "[Why Programming is Hard to Teach]({{ site.root }}/files/papers/guzdial-why-hard-to-teach-2011.pdf)" : A chapter from Making Software that explores why programming seems so much harder to teach than some other standard subjects.

Kirschner et al: "[Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-Based, Experiential, and Inquiry-Based Teaching]({{ site.root }}/files/papers/kirschner-minimal-guidance-fails-2006.pdf)" : Argues that inquiry-based learning is less effective for novices than guided instruction.

Mayer and Moreno: "[Nine Ways to Reduce Cognitive Load in Multimedia Learning]({{ site.root }}/files/papers/mayer-reduce-cognitive-load-2003.pdf)" : Shows how research into how we absorb and process information can be applied to the design of instructional materials.

Porter et al: "[Success in Introductory Programming: What Works?]({{ site.root }}/files/papers/porter-what-works-2013.pdf)" : Summarizes the evidence that three techniques---peer instruction, media computation, and pair programming---can significantly improve outcomes in introductory programming courses.

Wiggins and McTighe: "[UbD in a Nutshell]({{ site.root }}/files/papers/wiggins-mctighe-ubd-nutshell.pdf)" : A four-page summary of the authors' take on reverse instructional design.

Wilson et al: "Best Practices for Scientific Computing" : Describes and justifies the practices that mature scientific software developers ought to use.

Wilson: "Software Carpentry: Lessons Learned" : Summarizes what we've learned in 17 years of running classes for scientists.