Review of Griffiths, C. & Soruç, A. (2020) Individual differences in language learning: A complex systems theory perspective. Palgrave Macmillan.
Bibliographic record
Abstract
Individual Differences in Language Learning: A Complex Systems TheoryPerspective presents a wholly contemporary analysis of individual differences (IDs) in second language acquisition (SLA).Griffiths and Soruç approach this topic through a novel lens, positing that IDs in SLA are not isolated phenomena but complex interwoven processes.This argument is developed throughout, scrutinizing IDs through a complex dynamic systems theory (CDST) perspective and hypothesizing that IDs are interrelated such that a change in one will likely spur change in others.The text situates itself firmly in modern discourse on IDs as a multifactorial component of SLA, providing timely insights on trends in second language (L2) research with appropriate considerations for educators and learners.Two conceptual chapters bookend eleven central chapters that each profile a different ID.Chapter 1 gives an overview of foundational research, parameters for the book, the theoretical perspectives that drive the work, and short synopses of the coming chapters.The authors briefly introduce the CDST perspective of IDs, noting that the text will focus on one ID at a time to mitigate the potential chaos that comes with research in complexity theory, and then outline the eleven profiled IDs.Chapter 2 examines age as a variable by juxtaposing young, adolescent, and adult L2 learners.The authors note that perceptions of age have shifted over time, with early research often concluding that youth is advantageous for L2 development and more recent research suggesting that since age interacts with varied affective factors, conclusions should seek to contextualize the impact of age on language learning.Chapter 3 explores sex and gender, delineating that while sex is a biological construct, gender is a cultural construct.The authors therefore discuss that the effects of encultured gender on L2 development should not be ignored.The authors contend that the research shows little difference in language learning abilities between the sexes and conclude that gender should be considered as one factor that may interact with other IDs.Chapter 4 focuses on four overlapping, non-malleable IDs: race/ethnicity/nationality/culture.This chapter approaches the IDs through research into culture shock in a foreign country and how it affects communication in the target language, finding that proficiency plays an integral role in understanding how well a learner may assimilate into the target culture.Chapter 5 considers aptitude, examining learners' capacity for L2 development through profiles of exceptional language learners' IDs and practices, finding overlapping patterns of age, working memory, motivation, time on task, and sociocultural interaction.Chapter 6 explores personality, questioning whether there is a direct correlation between personality and language proficiency.In this chapter, the authors report that motivation, willingness to communicate, and ego were more prominent factors in SLA than personality itself.Likewise, chapter 7 defines learning style as learners' habits and preferences for information processing and suggests that while styles are typically considered stable, learners can be taught to style stretch.Furthermore, this chapter presents evidence that those students who can style stretch tend to score better.Chapter 8 examines learning strategies, considering the role of consciousness and type in strategies and presents CJAL
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".