Running head: TEACHING STRATEGIES FOR DIVERSE LEARNERS
Teaching Strategies for Diverse Learners:
Representing Computational Problem Types in High School Computer Science
Kevin Lacey
EDLC 510
Professor Hackman
September 14th, 2025
Teaching Strategies for Diverse Learners: Representing Computational Problem
Types in High School Computer Science
Instructional Overview
Class profile. Mr. Thompson’s high school computer science class (28 students) in an
urban magnet program includes a broad range of learners: 4 identified as gifted, 7
students with IEPs or 504 plans for processing or attention, 5 multilingual learners
(WIDA 2–5), and several students with ACEs histories.
Virginia Standard of Learning.
MSCSE-18.AP.1 – The student will improve existing solutions to problems to create new
programs.
a. Categorize problems as classification, prediction, combinational search, or sequential
decision problems.
b. Determine when problems can be solved with programs and automation.
c. Create a variety of programs while considering the needs and preferences of diverse
user groups.
d. Utilize existing code, media, and libraries into original programs, and give attribution.
Integration area: Represent.
Students will represent problem types and programming approaches using flowcharts,
pseudocode, annotated code snippets, and graphic organizers. Representations make
abstract algorithmic thinking visible, help compare approaches, and provide common
reference points across diverse learners.
Gifted Learners
Gifted students often detect patterns quickly, generalize across domains, and thrive on
novelty and challenge.
Strategy 1: Cross-domain representation design.
Gifted learners create multi-representation models (e.g., a Venn diagram showing overlap
between classification and prediction, annotated pseudocode highlighting decisions).
They compare representations across disciplines, deepening abstract reasoning.
Strategy 2: Open-ended optimization.
Gifted students propose representational improvements to existing solutions (e.g.,
redesigning a decision-tree diagram for clarity, annotating libraries with metadata for
reuse). Research shows that advanced learners benefit from open-ended design
challenges that require flexible representation of concepts across multiple forms (Lee,
2024).
Struggling Learners
Some students may need scaffolded steps, explicit models, and retrieval-based review to
reduce cognitive load.
Strategy 1: Scaffolded CRA for algorithms.
Use Concrete–Representational–Abstract (CRA) progression: (a) tangible analogies
(sorting cards for “classification”), (b) represent with simple flowcharts or T-charts, (c)
transition to abstract pseudocode. Worked examples reduce cognitive load and help
students internalize patterns more efficiently than unguided problem solving (Chen et al.,
2025; Lee, 2024).
Strategy 2: Guided templates for representations.
Provide graphic organizers (pre-drawn flowchart frames, partially filled pseudocode) that
reduce extraneous load while still requiring students to complete steps. Research in
computer science education shows scaffolds such as Parsons Problems help struggling
learners by providing structured representations to guide coding tasks (Hou et al., 2023).
English Language Learners (ELLs)
Multilingual students benefit from visuals, structured talk, and dual-language resources.
Strategy 1: Bilingual labeled diagrams.
Students create dual-language flowcharts and pseudocode commentaries (English plus
home language labels). Sentence frames support explanation:
- WIDA 2: “This is a ___ problem because ___.”
- WIDA 4: “The representation shows a sequential decision because ___.” Asset-based
approaches that value multilingualism in STEM classes improve comprehension and
student engagement (Mouboua et al., 2024).
Strategy 2: Structured partner explanation.
ELLs pair up: one builds/represents the problem, the other explains it aloud using a word
bank (classification, prediction, decision, automation, program). Such structured talk
routines support both academic language and conceptual understanding (Hoffman et al.,
2021).
Students Impacted by Trauma
Trauma can affect attention, trust, and regulation. Predictability, autonomy, and choice
reduce barriers.
Strategy 1: Predictable representational routines.
Begin each lesson with a calm start plus preview slide showing the representation goal
(e.g., “Today: Represent classification with flowcharts”). Consistent icons and colors
build routine and safety. Whole-school trauma-informed routines have been shown to
improve student readiness to learn and reduce teacher burnout (MacLochlainn et al.,
2022).
Strategy 2: Choice-based representation menu.
Offer options: represent via flowchart, pseudocode, or diagram. Students choose their
preferred mode, then add a caption. Choice and autonomy lower stress and increase
ownership, key principles in trauma-informed education (Stokes, 2022).
Putting It All Together: Learning Environment Modifications
- Stations: one for flowcharts, one for pseudocode, one for diagrams. Gifted students
extend with cross-domain links; strugglers use scaffolds; ELLs use bilingual supports;
trauma-impacted students exercise choice.
- Anchor wall: collective chart displaying student-created representations of each
problem type.
- Assessment: single-point rubric on accuracy and clarity of representations, not just
coding syntax.
Conclusion
Grounding MSCSE-18.AP.1 in represent ensures every learner has access to
computational problem solving. Gifted students extend through cross-domain design,
struggling learners benefit from CRA scaffolds, ELLs gain language support, and trauma-
impacted students thrive with predictable, choice-driven routines. Representation
becomes the unifying strategy for equitable access.
References
Chen, C.-Y., et al. (2025). Effects of worked examples with explanation types and
novices’ motivation on cognitive load. Proceedings of the ACM on Human-Computer
Interaction. https://dl.acm.org/doi/abs/10.1145/3732791
Hoffman, L., Suh, E., & Zollman, A. (2021). What STEM teachers need to know and do
to engage families of emergent multilingual students. Journal of STEM Teacher
Education, 56(1). https://ir.library.illinoisstate.edu/jste/vol56/iss1/2
Hou, X., Ericson, B. J., & Wang, X. (2023). Understanding the effects of using Parsons
Problems to scaffold code writing for students with varying CS self-efficacy levels. arXiv
preprint. https://arxiv.org/abs/2311.18115
Lee, H. M. (2024). The worked-example effect and a mastery approach goal orientation.
Education Sciences, 14(6), 597. https://doi.org/10.3390/educsci14060597
MacLochlainn, J., et al. (2022). An evaluation of whole-school trauma-informed training:
Effects on school personnel. International Journal of Environmental Research and Public
Health, 19(15), 9360367. https://pmc.ncbi.nlm.nih.gov/articles/PMC9360367/
Mouboua, P. D., Atobatele, F. A., & Akintayo, O. T. (2024). Bridging STEM and
linguistic gaps: A review of multilingual teaching approaches in science education.
Research Journal of Multidisciplinary Studies, 7(2), 86-97.
https://doi.org/10.53022/oarjms.2024.7.2.0030
Stokes, H. (2022). Leading trauma-informed education practice as an instructional model
for teaching and learning. Frontiers in Education, 7, 911328.
https://www.frontiersin.org/articles/10.3389/feduc.2022.911328/full
