Tracing pre-service teachers’ understanding of specialized content knowledge for sequencing and structuring mathematics learning in low-resource Malawi classrooms
Bibliographic record
Abstract
While teaching quality is essential across all educational contexts, it holds particular significance in under-resourced and structurally challenged environments such as Malawi, where the education system is still developing. Despite this, little is known about how Malawian pre-service teachers develop their understanding of the knowledge and skills needed to teach mathematics in primary schools. This study investigates how two first-year pre-service teachers in Malawi approached the implementation of two mathematics teaching tasks, shedding light on both shared and divergent understandings of the knowledge required to sequence and structure students’ mathematics learning—a key component of Specialized Content Knowledge (SCK). Drawing on data collected from a teacher education college and the primary schools where the participants completed their supervised teaching practice, we employed thematic analysis informed by the practice-based theory of mathematical knowledge for teaching. Findings indicate that pre-service teachers primarily relied on their training experiences, particularly the guidance received from teacher education colleges and the utilization of low-cost instructional resources. This reliance often limited their responsiveness to students’ varied learning needs and restricted their ability to create opportunities for conceptual connections across mathematical topics and representations. However, as their teacher education progressed, participants began to develop a more refined understanding of the demands of teaching. They became increasingly aware of the complexities and contextual specificities of the knowledge and skills required for effective mathematics instruction in primary classrooms. Importantly, the study highlights how their evolving grasp of SCK enabled a more adaptive approach to the sequencing and structuring of students’ mathematics learning. These findings underscore the importance of fostering reflective, practice-based experiences in teacher education and point to the need for further research on the role of SCK in mathematics teacher preparation in resource-constrained educational environments. They also highlight the importance of conducting further research to better understand how SCK can be more effectively developed and implemented in mathematics teacher education programs in Malawi.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".