The aim of this article is to review the state of the art of research and theory development on student learning patterns in higher education and beyond. First, the learning patterns perspective and the theoretical framework are introduced. Second, research published since 2004 on student learning patterns is systematically identified and reviewed. This part includes two main sections. In the first section, new evidence on internal and external relationships of learning patterns is reviewed. Four themes are covered here: the dimensionality and the internal relationships of learning patterns and relationships of learning patterns with personal, contextual, and outcome variables. In the second section, new directions in learning patterns research are examined. These include studies on learning patterns in new international contexts and populations, longitudinal development of learning patterns over time, methodological advances in learning patterns research, and studies on fostering the quality of students’ learning patterns. Next, relationships with adjacent theories on student learning are discussed, the learning patterns perspective is critically examined, and pathways are derived to move the model forward. Finally, future conceptual and methodological directions for learning patterns research are derived.

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A Learning Patterns Perspective on Student Learning in Higher Education: State of the Art and Moving Forward

This Handbook describes the extent and shape of computing education research today. Over fifty leading researchers from academia and industry (including Google and Microsoft) have contributed chapters that together define and expand the evidence base. The foundational chapters set the field in context, articulate expertise from key disciplines, and form a practical guide for new researchers. They address what can be learned empirically, methodologically and theoretically from each area. The topic chapters explore issues that are of current interest, why they matter, and what is already known. They include discussion of motivational context, implications for practice, and open questions which might suggest future research. The authors provide an authoritative introduction to the field and is essential reading for policy makers, as well as both new and established researchers.

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The Cambridge Handbook of Computing Education Research

Novice Programmers and Introductory Programming

A Study Design Process

Programming is a central concern of computer science, so its medium—programming languages—should be a focus of computing education. Unfortunately, much of the community lacks useful tools to understand and organize languages, since the standard literature is mired in the ill-defined and even confusing concept of paradigms. This chapter suggests the use of notional machines, i.e., human-accessible operational semantics, as a central organizing concept for understanding languages. It introduces or re-examines several concepts in programming and languages, especially state, whose complexity is understood well in the programming languages literature but is routinely overlooked in computing education. It identifies and provides context for numerous open problems worthy of research focus, some of which are new twists on long-running debates while others have not received the attention in the literature that they deserve.

Programming Paradigms and Beyond

Background

Students' beliefs about their intellectual ability influence their use of learning strategies, learning effort, and response to failure or setbacks. Students with incremental views of intelligence believe that learning is possible with sufficient effort, whereas those with entity views believe that intelligence is a fixed quality and expenditure of effort reflects an insufficient amount of that quality.



Purpose

This study examined the relationship between engineering students' beliefs about intelligence and their perceived use of active learning strategies such as collaboration and knowledge-building behaviors, self-efficacy for learning and performance, and course grade. The study also examined the extent of entity and incremental beliefs in a sample of engineering students.



Design/Method

The correlational study analyzed data from 377 engineering students recruited from required engineering courses at a large public university. We used bivariate correlations to examine relationships between study variables and multiple regression analyses to examine predictive ability of the variables on learning strategies and course grade.



Results

Our results showed that students' intelligence beliefs were correlated with active learning strategies. Self-efficacy, reported use of collaboration, and incremental beliefs about intelligence were predictive of students' reported use of knowledge-building behaviors. Intelligence beliefs were not predictive of course grade.



Conclusions

Our results demonstrate the utility of these motivational beliefs for understanding university engineering students' learning efforts. Our results also suggest a need for instructors to support incremental views of intelligence among engineering students.

Marcia Corby

Papers

Engineering Students' Intelligence Beliefs and Learning