Alexandria's Notes

"Learning Sciences for Computing Education"

11 min read

Summary: Chapter 8, "Learning Sciences for Computing Education"

This chapter provides an introduction to the theories, methods, and practices of the learning sciences, particularly as they relate to computing education, to help those who are unfamiliar with the learning sciences discover the connections between these fields.1

8.1 Introduction

The learning sciences emerged in the 1990s and are multidisciplinary, falling under education, psychology, computer science, edtech, linguistics, and data analytics.2

There are many different definitions of learning: Cognitive psychology and neuropsychology tend to define learning as a change in the brain— a development of neural architecture and synapses. Those from CS and edtech tend to define learning as a master of a sequence of concepts. Those from education and linguistics tend to define learning as a change in experience— a change in what learners can accomplish and their attitudes about topics or situations.3

Both the learning sciences and computing education emerged from cognitive science.

The learning sciences and computing education can trace their roots back to a related field: cognitive science. Cognitive science emerged in the 1960s from a combination of fields (see Chapter 9). The two that are relevant here are cognitive psychology and computer science.4

The first learning scientists argued that the cognitive science approach to researching learning was too sterile to be applied to authentic learning.

Authentic learning includes not only cognitive factors, but also the environment, the instructor, fellow learners, personal attitudes and beliefs, and use of technology (Kolodner, 2004). Therefore, the learning sciences broke from the traditions in cognitive science of highly controlled experiments in lab settings to embrace new practices of application-minded design experiments that are less controlled (and less scientifically rigorous), but more generalizable to authentic learning environments (Hoadley, 2004).5

8.2 Theoretical Foundations

Section 8.2 will introduce the theoretical foundations of the learning sciences.

In this section, we introduce some of the underlying theoretical foundations of the learning sciences to discuss what the computing education research community can learn about theory from the learning sciences. The four theories discussed here have long histories of empirical work and represent major components of learning and learning environments. Constructivism addresses how learners cognitively build knowledge; cognitive apprenticeship addresses how instruction scaffolds learners’ emerging skills and knowledge; sociocultural theory addresses the social and environmental aspects of learning environments; and expectancy-value theory addresses the role of motivation in learning. Of course, these components do not exist in isolation in the learning environment, and similarly, these theories interact with each other.6

8.2.1 Constructivism

Constructivism is a common educational theory about how people cognitive acquire knowledge. “In essence, constructivism states that people learn best when they construct knowledge for themselves rather than being told explicitly what to know and how to learn it.”

“Construct knowledge for themselves” means that learners are making sense of new information through reasoning and invoking their prior knowledge rather than being told how to interpret and organize new information, as is common in more direct instruction (i.e., instruction in which the instructor explicitly tells the students everything that they need to know). It also means that students are learning concepts and skills through exploration that is guided by an instructor but not prescribed by an instructor, as it would be in more direct instruction. The instructor can still have learning objectives, but there are multiple paths to achieve them. In the definition, “learn best” has several different meanings. It means that constructivist approaches help learners perform better on tasks and tests by increasing their depth of thought and connections to prior knowledge, resulting in better retention and transfer of knowledge (Bruner, 1973; see more about transfer in Chapter 9). It also means improving motivation and emotion by increasing student agency in learning and helping them connect knowledge to their lives (Searle & Kafai, 2015).7

There is a longstanding debate between constructivists and direct instructionists. Direct instructionists view learning as changes in the brain. Constructivists view learning more holistically, including development of professional and soft skills— this is much harder to test, leading to criticism of scientific rigor. In reality, researchers and educators tend to tactically use a blend of approaches.

At the center of this debate is a fundamental difference in the definition of learning. The direct instructionists view learning as a change in the brain caused by the storage of new information, and they therefore argue that direct instruction is the most efficient and easiest method for learning. The constructivists view learning as a change in knowledge that is not worth much without a concomitant change in professional skills (e.g., solving authentic problems) and soft skills (e.g., working collaboratively). The latter is much harder to study in true experiments than the former, leading to criticism of scientific rigor by the direct instructionists. Constructivists argued, however, that scientific rigor is not worth research that is conducted in sterile environments (i.e., labs) that are fundamentally different from the authentic environments (e.g., classrooms) in which the research will be applied (discussed further in Section 8.3.1 on design-based research; Brown, 1992). As with most debates, many researchers and educators fall in the middle, recognizing the contributions of both types of instruction and treating them as two ends of a spectrum. Therefore, instruction can be more direct or more constructive depending on the needs of the learner and what is most appropriate.8

Constructionism is based on constructivism but they are not equivalent. It stipulates that the learner should externally construct artifacts to aid the internal construction of knowledge structures.9 Constructivism primarily involves the internal construction of knowledge. Constructionism extends this by adding the creation of a physical or digital artifact. (e.g. building a model volcano or writing a small app.)

8.2.2 Cognitive Apprenticeship

The vast majority of learning throughout history was structured between an domain expert and an apprentice. The apprentice learns via observation and deliberate, sequenced practice under the expert’s guidance.10 (e.g. Carpentry, midwifery, private tutoring) However, apprenticeship model is hard to recreate because 1) it relies on small student-teacher ratio and 2) the knowledge and skills are scoped to a narrow domain.

Cognitive apprenticeship seeks to integrate practices from traditional apprenticeships.

Cognitive apprenticeship was proposed as a means to integrate the successful practices of traditional apprenticeships with the more general knowledge and cognitive skills sought by traditional school settings (Collins, Brown, & Newman, 1989). This approach to orchestrating a learning environment holistically considers four unique components: content, method, sequence, and sociology (Collins & Kapur, 2014). Content in this sense is concerned not only with domain knowledge, but also with the heuristic strategies used by experts within the domain to solve problems, the metacognitive control strategies used to monitor one’s progress while completing a task, and the more general strategies to learn new things.11

Cognitive apprenticeship considers the careful sequencing of learning activities. Learners can first be instructed to consider the big picture first and then scaffolding can abstract away smaller-picture details. Lastly, learning environments are social in nature, with content situated in real-world contexts and explored by communities of learning.

Consistent with Vygotsky’s constructivist Zone of Proximal Development (1978), cognitive apprenticeship learning environments consider the careful sequencing of learning activities to increase the task complexity and diversity of skill learners develop alongside their growing abilities. Additionally, considering global skills (rather than local skills) first helps orient the learner toward the big-picture tasks to be addressed (Collins & Kapur, 2014). Scaffolding can be provided to abstract away the local skills early on, and learners gradually see more detail as they progress. Lastly, these learning environments embrace the socially embedded and cooperative nature of learning seen in traditional apprenticeship environments. Content is situated in real-world contexts and explored by communities of learners (see Section 8.2.3 of this chapter) while fostering learners’ intrinsic motivation (see Chapter 28 of this volume).12

“Productive failure is a learning design that formalizes the process of learning from one’s mistakes.” By engaging learners in problems beyond their current abilities, and then integrating these attempts with established solutions, productive failure facilitates deep and transferable knowledge.13

8.2.3 Sociocultural Theory

Sociocultural theory is a group of theories that emphasize learning as an activity embedded within social, cultural and historical context and interaction with others and with available resources. They focus on real-world contexts and addressing issues of power and equity.14

Situated learning

Learning occurs within a community, moving from the periphery to full engagement and identifying with the community. This is useful for providing scaffolding for novices by providing a context for doing computing. (A programmer might work on a less intensive project.) It’s also useful for addressing “identity gaps” for underrepresented groups.

Activity theory

Learning occurs through interactions within an activity system that includes the learner, the learning objectives, and the tools and community involved.

8.2.4 Expectancy-Value Theory

“Expectancy-value theory is a motivation theory to explain choice, persistence, and performance.” (Why they make decision? How long do they stick with it? How well do they do?)

Expectancy is about a person’s belief in their own ability to complete a task successfully. If they think they can do it, they’re more likely to be motivated.

Value refers to how much a person values the task.

When both expectancy and value are high, a person is more motivated to engage in the task. When either is low, motivation decreases and may depend on specific circumstances.

8.3 Methodology (Design-Based Research)

Design-based research (DBR) is a particularly common research method, used to capture the complexities in learning sciences research. Using DBR, a team of researchers will identify a learning problem and potential solution. The team will then develop an intervention to address the problem for a selected group. The team will then adjust and iterate, as needed, to improve the outcome. This will then be adapted for another group. Through several iterations, a generalizable intervention for a broader population will develop.[]

Design-based research stands out from other research methodologies due to its focus on:

  • Real-world contextual relevance.
  • Iterative nature: Iteratively refining an intervention and re-testing some variable.

8.4 Stages of Learning Sciences Projects

empirical projects in the learning sciences are made up of four stages: (1) conducting studies to better understand a learning context and its learners; (2) designing initial interventions based on these findings; (3) iterating on the designs based on lessons learned during empirical trials; and (4) scaling up a well-tested intervention beyond the local context in which it was refined to contribute to theory.15

8.5 Conclusion

Learning sciences and computing education research have a lot in common and have a lot to learn from and benefit from each other. Integration between the disciplines would increase the impact of both and increase computing literacy for everyone.

Footnotes

  1. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 211). Cambridge University Press. Kindle Edition. ↩

  2. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 208). Cambridge University Press. Kindle Edition. ↩

  3. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 208). Cambridge University Press. Kindle Edition. ↩

  4. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 209). Cambridge University Press. Kindle Edition. ↩

  5. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 210). Cambridge University Press. Kindle Edition. ↩

  6. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 211). Cambridge University Press. Kindle Edition. ↩

  7. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 212). Cambridge University Press. Kindle Edition. ↩

  8. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (pp. 212-213). Cambridge University Press. Kindle Edition. ↩

  9. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 213). Cambridge University Press. Kindle Edition. ↩

  10. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 213). Cambridge University Press. Kindle Edition. ↩

  11. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 214). Cambridge University Press. Kindle Edition. ↩

  12. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 215). Cambridge University Press. Kindle Edition. ↩

  13. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 216). Cambridge University Press. Kindle Edition. ↩

  14. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 216). Cambridge University Press. Kindle Edition. ↩

  15. Fincher, Sally A.; Robins, Anthony V.. The Cambridge Handbook of Computing Education Research (Cambridge Handbooks in Psychology) (p. 223). Cambridge University Press. Kindle Edition. ↩

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