Motivation 

To evaluate whether explicit instruction followed by problem solving or problem solving followed by explicit instruction is more effective for later problem-solving performance, especially for procedures that are inherently complex.

Order of Problem Solving and Explicit Instruction

The debate between explicit/direct instruction and minimal instruction is longstanding in problem-solving education. Those who support direct instruction (i.e., explicitly telling the learner everything you want them to know) cite its efficiency for producing gains in problem-solving skills. Those who support minimal instruction (i.e., providing scaffolding to the learner to encourage them to construct problem-solving knowledge themselves) cite the enduring effects of building upon prior knowledge and development of other skills throughout the process. A subgroup has decided both types of instruction are important and now debates in which order learners should receive both types of instruction.

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Motivation 

To propose a theory of the cognitive mechanisms responsible for the relationship between spatial skill and STEM achievement.

Spatial Skill and STEM Achievement

Decades of work show that high achievers in STEM, e.g., chemistry, physics, geology, computer science, have high spatial skill, e.g.,

• spatial visualization, like mentally rotating or folding an object
• spatial relations, like using a map to plan a route
• spatial orientation, like following a route.

Moreover, improving spatial skill through training broadly improves STEM performance. This type of broad transfer from one type of cognitive training to many types of problem-solving tasks across multiple domains is exceedingly rare. I can’t think of a factual analogy for it, so I’ll give you a fake one. It’s like if memorizing the digits of pi helped you solve any problem that included a number, whether it was in solving equations in math, finding proportions in art, using measurements in science, or counting beats in music. We still don’t understand the cognitive mechanisms responsible for this relationship, though, so I pulled together literature from psychology, discipline-based education research, learning sciences, and neuroscience to propose a theory. I’ll summarize the main points from each area before presenting the theory.

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Preamble for those interested in how I made it through 8 years of computing education research without knowing how to program:

When I started my research career in psychology, I knew nothing about computer science. I had chosen to do my PhD with Richard Catrambone, a world-class cognitive scientist doing cool work at the intersection of cognitive psychology and educational technology. In my first month, I agreed to be a research assistant on a project about applying educational psychology to computing education between Richard and Mark Guzdial, a renowned computing education researcher. To me at the time, Mark was just some professor, and computing *probably* had to do with computers. I still remember our first meeting when Mark asked me if I had any programming experience. I said I had worked a little bit with HTML (and not that it had to been to customize my MySpace page). He gently told me that didn’t really count for what we were doing, and I tried to figure out why but couldn’t. That’s how little I knew.

So how on Earth have I conducted computing education research from that day forward? Partly with fearlessness stemming from sheer ignorance, but mostly with tons of help from people with loads of experience and knowledge about computing and teaching computing. While at Georgia Tech, I worked with Mark, Briana Morrison, and Barbara Ericson, who each have more computing education knowledge than any one person has a right to. Working with them, the most valuable perspective I had was as a novice. I could empathize with learners because I knew just as little as they did. Continue reading →

Briana Morrison, Adrienne Decker, and I have a couple of conference papers coming out this summer based on our work funded by NSF IUSE (1712231 and 1927906) for developing and testing subgoal labeled worked examples throughout introductory programming courses. We’re finishing up the second of three years on the project, so now is a good time to provide an update on the project and summaries of the papers. 

Project Update

Our goal for this project was to identify subgoals for programming procedures taught in introductory programming courses, create subgoal labeled materials that are easily adopted by other instructors, and test the efficacy of those materials for learning. During the first year of the grant, we conducted the Task Analysis by Problem Solving protocol to identify the subgoals of intro programming procedures. I’ve been holding back from taking an intro programming course for years just so that I could be a novice for this activity. For the task analysis, Briana taught me the basics of a semester-worth of programming in Java in one week while I visited her in Omaha. If there is anyone who could teach a semester in one week, it’s Briana. After the task analysis, we used the subgoals to develop subgoal labeled worked examples and practice problems that started from the most basic problems and gradually increased in complexity.

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Motivation 

To explore how physical representations of symbolic relationships affect accuracy in symbolic reasoning.

Symbolic Reasoning

Symbolic reasoning is a higher order thinking skill that allows us to reason about abstract structures and relationships without relying on a concrete, physical context (e.g., evaluating whether a + b * c + d = d + c * b + a). Because symbolic representations are still physically written on paper, screens, etc., Landy and Goldstone argue that they are subject to biases based on their physical representations, despite differences in physical representations being completely irrelevant to symbolic reasoning. For the equation above, most people would more quickly evaluate whether a + b*c + d  =  d + c*b + a, even though the space between symbols does not affect their relationship. Another example is data visualization. Despite representing the same data, different graph designs can affect how viewers interpret data.

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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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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 →

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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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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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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