dynamic-assessment
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- PaperReCALL13 Jul 2026
The effectiveness of computerized dynamic assessment in improving L2 performance: A three-level meta-analysis
Qi Lu, Mengqi Chen, Lianrui Yang, Shaofeng Li et al.
A three-level meta-analysis of 35 studies found that computerized dynamic assessment (C-DA) has large positive effects on L2 performance, with cake format (mediation embedded in the test) yielding larger effect sizes than sandwich format (mediation between pretest and posttest). Moderator analyses identified number of items, test content, and learners' first language as significant factors influencing C-DA effectiveness.
Original abstract
The growing body of research on the effects of computerized dynamic assessment (C-DA) on second language (L2) learning underscores the need for a comprehensive research synthesis to identify future research directions and inform the application of C-DA in L2 educational contexts. This meta-analysis employed a three-level modeling approach to examine the effectiveness of C-DA in improving L2 learners’ performance. It synthesized 27 effect sizes from cake format designs, in which mediation is embedded within the test sequence, and 24 effect sizes from sandwich format designs, where mediation is delivered between a pretest and a posttest, across 35 studies published between 2000 and May 27, 2025. This study also investigated the key variables that moderate C-DA effectiveness. Findings reveal large, significant positive effects of both the cake and sandwich formats on L2 performance improvement (cake format: g = 2.120, p < .001; sandwich format: g = 1.676, p < .001), with the cake format tending to yield larger effect sizes. This may be because the cake format captures gains during mediation, whereas the sandwich format reflects post-mediation outcomes. Moderator analyses show that the number of items, test content, and learners’ first language affect C-DA effectiveness in promoting L2 performance. Drawing on the synthesized findings, this study contributes to theoretical, methodological, and technological understandings of C-DA and offers suggestions for future research in this domain.
- PaperRELC Journal9 Jul 2026
Using dynamic feedback from customized LLM-powered chatbots in EFL argumentative writing
Yi Chen
An innovative teaching practice used two custom-built LLM-powered chatbots to provide dynamic, instruction-aligned feedback on argumentation quality and linguistic accuracy in an EFL argumentative writing course, supporting iterative multi-draft revision while keeping instructors in the loop to address automated feedback limitations.
Original abstract
With the growing use of large language models (LLMs) in English as a foreign language (EFL) teaching, balancing teacher control over instructional focus with students’ autonomy in using artificial intelligence chatbots while ensuring more dynamic interaction have become key pedagogical concerns. This article reports on an innovative teaching practice implemented in a foundation undergraduate English for Academic Purposes course, which featured interactive feedback, including chatbot-mediated dynamic assessment, delivered through two custom-built LLM-powered chatbots. The intervention aimed to support students’ argumentative writing with specific reference to argumentation quality and linguistic accuracy, through an iterative multi-draft process. Drawing on observation and critical reflection on this practicum, the article argues that customized LLM-powered chatbots can provide dynamic, instruction-aligned feedback at scale to scaffold EFL learners’ writing process, while instructors remain essential in the loop to address the limitations of automated feedback. The article concludes with suggestions for refining this approach in future practice.
- PaperLanguage Testing26 Jun 2026
Developing and Validating a Computerized Dynamic Diagnostic Assessment of Pragmatic Competence for Chinese Learners of English
Qi Lu, Ying Chen, Lianrui Yang
A computerized dynamic diagnostic assessment of pragmatic competence (CDDA-P) was developed and validated using the example of refusals for Chinese learners of English. The assessment employed empirically derived response options and demonstrated fine-grained diagnosis of learners' strengths, weaknesses, and zones of proximal development, leading to significant improvement in learners' pragmatic performance.
Original abstract
Recent initiatives have sought to integrate dynamic assessment and diagnostic assessment to facilitate language learning. Extending this line of innovation, the present study uses the example of refusals to illustrate the development and validation of a computerized dynamic diagnostic assessment of pragmatic competence (CDDA-P). To enhance the authenticity of assessed pragmatic performance, a bottom-up approach was adopted in item design, in which response options were empirically derived from a corpus of productions by 335 language users. The effectiveness of the CDDA-P was examined from three key perspectives: its ability to diagnose learners’ strengths and weaknesses, to identify their zones of proximal development (ZPDs) and learning potential, and to promote the development of learners’ pragmatic performance. A pretest–immediate posttest-delayed posttest design was employed to track changes in 66 Chinese learners’ performance before and after the implementation of the CDDA-P. Findings reveal that the CDDA-P can provide a fine-grained diagnosis of learners’ strengths and weaknesses in performing L2 refusals and identify their diverse ZPDs and learning potential. Furthermore, learners demonstrated significant improvement after mediation. This study presents both theoretical and methodological implications for the development of integrated dynamic and diagnostic language assessment, while also offering insights into promoting L2 pragmatic competence through assessment.
- PaperBritish Journal of Educational Technology20 Apr 2026
Step‐by‐step towards understanding artificial intelligence: A scaffolded learning progression for young learners
Srijita Chakraburty, Cindy Hmelo‐Silver, Krista Glazewski, Anne Leftwich et al.
Examined a scaffolded learning progression for AI literacy in upper elementary students, using dynamic assessment to track conceptual shifts from surface-level to data-centered reasoning. Found that structured tasks and facilitatory prompts helped students move toward more accurate understandings of how AI collects and uses data.
Original abstract
Artificial intelligence (AI) is increasingly shaping how young learners interact with digital technologies, yet many upper elementary students engage with AI systems passively and develop intuitive and sometimes inaccurate conceptions of how these systems work. This study examines the Foundational AI construct within a refined learning progression (LP), exploring how scaffolded instruction and dynamic assessment support conceptual shifts in students' understanding of how AI collects, learns from and uses data to make decisions. Drawing on Vygotsky's zone of proximal development and synergistic scaffolding theory, we refined the foundational AI construct of a five‐level LP and designed a two‐phase activity grounded in this LP to elicit and support student reasoning through structured tasks, informational scaffolds and facilitator prompts. Through mixed methods analysis of clinical interviews with 13 fourth and fifth graders (9–11 years), we identified recurring misconceptions and tracked shifts in student reasoning and movement along the Foundational AI construct of the LP. Furthermore, we examined one student's trajectory in depth to illustrate how dynamic assessment can function as a responsive instructional tool. Findings provide initial empirical insight into how scaffolded LP‐aligned instruction, paired with dynamic assessment, can support young learners' movement from surface‐level ideas to more structured understandings of how AI systems function. These insights contribute to the design of developmentally appropriate and contextually responsive AI learning experiences for primary education. Practitioner notes What was already known about this topic? Many young learners interact with AI technologies (eg, voice assistants, recommendation systems) but often hold surface‐level or inaccurate conceptions of how AI works. AI literacy frameworks exist, but none currently provide scaffolded pathways that align with young students' developmental readiness or explicitly address their initial misconceptions What this paper adds? Provides an initial empirical examination of a refined five‐level Foundational AI construct within a broader Learning Progression (LP) for upper elementary students. Demonstrates how LP‐aligned scaffolded instruction, using tasks, just‐in‐time informational supports and decision trees, can guide students from intuitive ideas to more data‐centered reasoning. Uses dynamic assessment to track and support conceptual growth, providing insight into students' readiness to reason about AI systems. Implications for practice and/or policy Scaffolded LPs that integrate structured tasks, informational prompts and dialogic facilitation can help support developmentally grounded AI instruction that is responsive to learner needs. Dynamic assessment frameworks can help researchers and educators capture students' shifts in reasoning, differentiating between ideas students can articulate independently and those requiring additional support. Designing layered, responsive scaffolds that actively elicit student reasoning and provide opportunities for reflection can support educators in guiding students' conceptual growth in AI literacy.