Article Summary: Richley et al. (2019) Impact of Erroneous Examples

Motivation 

To explore the impact on performance and affect of explaining and correcting worked examples that include errors compared to practicing problem solving.

Erroneous Examples and Misconceptions

Erroneous examples, or worked-out solutions to an example problem that include at least one incorrect step, have been studied as a way to address misconceptions. Misconceptions can be hard to remedy with direct explanations. Instead, it is often more effective to allow the learners to uncover the logical flaw that disputes a misconception.

For instance, a common misconception in biology is that trees grow from nutrients that they pull from the soil. If an instructor explained that trees grow by breathing in CO^2 from the air, retaining the carbon, and breathing out O^2, a biology student is likely to forget the correct explanation. Instead,  if the instructor asks what trees are made out of (carbon), what a tree breathes in (CO^2), and what a tree breathes out (O^2), then the student makes the conclusion that trees grow from carbon in the air and is more likely to remember the correct explanation long term.

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Issue Summary: Advancing Theory about the Novice Programmer

I was a guest editor with Briana B. Morrison for a special issue of Computer Science Education on the topic “Advancing Theory about the Novice Programmer.” We had so many high quality submissions that it ended up being a double issue of exciting, current, and theory-driven research. In our guest editorial, we give a 1-paragraph summary of each of the six articles.

  1. Concepts before Coding: Non-Programming Interactives to Advance Learning of Introductory Programming Concepts in Middle School by Grover, Jackiw and Lundh
  2. Teaching Computer Programming with PRIMM: A Sociocultural Perspective by Sentance, Waite and Kallia
  3. Block-based versus Text-based Programming Environments on Novice Student Learning Outcomes: A Meta-Analysis Study by Xu, Ritzhaupt, Tian and Umapathy
  4. A Theory of Instruction for Introductory Programming Skills by Xie, Loksa, Nelson, Davidson, Dong, Kwik, Tan, Hwa, Li and Ko
  5. CS1: How Will They Do? How Can We Help? A Decade of Research and Practice by Quille and Bergin
  6. A Systematic Literature Review of Student Engagement in Software Visualization: A Theoretical Perspective by Al-Sakkaf, Omar and Ahmad

I don’t have permission to reprint the editorial here, but it is available for free on the journal’s website. Continue reading

Article Summary: Engeström & Sannino (2010) Theory of Expansive Learning

Motivation 

To describe the foundations of expansive learning, including but not limited to ideas from cultural-historical activity theory (CHAT), summarize 20 years of research using expansive learning as a theoretical framework, and explore future directions and challenges. I will focus on only the first of these objectives.

Theory of Expansive Learning: Classification

Expansive learning is a learning theory for circumstances in which organizations need to break the mold and radically change what they do and how they do it. Learning in this case typically means learning as professionals or members of another type of community, and it does not mean instructing students. Expansive learning spans many dimensions used to classify learning theories.

  1. Is the learner primarily an individual or member of a community?
  2. Is the learning primarily a process that transmits culture or transforms culture?
  3. Is the learning primarily a process of vertical improvement (get better at tasks within a pre-defined set of skills) or horizontal movement (learn tasks outside of disciplinary boundaries and hybridize different cultural contexts)?
  4. Is the learning primarily a process of acquiring or creating knowledge based on empiricism or of forming new knowledge based on theory?

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Chapter Summary: Mayer & Wittrock (2006) Problem Solving

Motivation 

To summarize the research on the teaching of problem solving–how people apply their knowledge to new situations, reason about scenarios for which they have incomplete or uncertain information, and solve novel problems.

Problem Solving Definitions

Problem solving: a cognitive process that is used to transform a given state into a goal state when a problem does not have an obvious solution, often used interchangeably with thinking and reasoning. Problem solving can be academic, such as solving an unfamiliar arithmetic word problem, or non-academic, such as how get 3/4 of 2/3 of a cup of cottage cheese.

Types of problems (well-defined vs. ill-defined): Well-defined problems have clearly specified given (problem) states, goal (solution) states, and problem-solving spaces (i.e., the relevant information required to solve the problem and the rules/logic/operators that connection different bits of information). For example, an arithmetic problem, no matter how complex, is well-defined. In ill-defined problems, the given state, goal state, or problem-solving space might be unclear. For example, writing an essay or designing a sustainable building are ill-defined problems. The knowledge of the problem solver does not determine whether problems are well- or ill-defined.

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Article Summary: Twining et al (2017) Guidance on Conducting and Reporting Qualitative Studies

Motivation 

To address a problematic “imbalance between the number of quantitative and qualitative articles published in highly ranked research journals by providing guidelines for the design, implementation, and reporting of qualitative research.” They also discuss the risks and benefits of a highly ranked research journal (Computers & Education) recommending guidelines to be used, albeit flexibly, in qualitative research.

Qualitative or Quantitative Methodology and Data

The paper starts by addressing common misconceptions about when it is appropriate to mix-and-match qualitative and quantitative. They define qualitative methodology as hermeneutic or interpretivist and based on a belief in the validity of multiple culturally-defined interpretations of multiple realities. Therefore, qualitative methodology is incompatible with quantitative methodology, which they define as objectivist or empiricist and based on a belief in the validity of one true explanation of one objective reality. Within each of these methodologies, however, data collection methods, instruments, and analysis can be both qualitative (i.e., non-numeric) or quantitative (i.e., numeric) and mixed-and-matched at will. Much more detail about these concepts and their relationships can be found at Twining’s blog post that extends their very useful Table 1.

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Article Summary: Chen et al. (2018) Computer Supported Collaborative Learning in 3 Meta-Analyses

Motivation 

To examine the factors that make computer supported collaborative learning (CSCL) environments effective. I’ll admit that the authors refer to this paper as a single meta-analysis, but I’d argue they’ve really done three meta-analyses with subsets of the same (large) set of papers. At the very least, I hope the amount of work that the authors put in isn’t the new standard for completing a meta-analysis.

Three research questions for CSCL

The authors chose to examine the following three research questions simultaneously based on the same set of papers because the efficacy of CSCL environments involves multiple, interrelated factors and can be compared to multiple, valid control groups. CSCL researchers manipulate only a subset of these factors in each study to determine the efficacy of specific interventions. While this controlled approach is scientifically sound, it means that a single study cannot compare CSCL environments to a range of possible alternatives. Therefore, the authors simultaneously considered 356 papers that included 425 studies to determine which features of CSCL are more effective compared to which alternatives.

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Helpful vs Unhelpful Responses When Someone Says They’ve Been Offended

This week in both my personal academic community and in the larger academic community, I saw people speaking publicly about behaviors that offended them. After reading the comments on both instances, I noticed a trend in what was helpful and not helpful. Instead of my usual article summary, I wanted to write a summary of my observations.

This week on academic Twitter, we saw this tweet.

too young to be a professor

I get this comment about once a semester (or way more if you count undergrads who email me starting with, “Hey Lauren,”), and I always find it offensive. I want to extra-emphasize that this is a comment that strangers make, and it has nothing to do with my personal qualifications. Instead, it reminds me that I belong to an underrepresented group in my profession and that regardless of our qualifications, some people find it strange that young women are professors. Continue reading