Miriam C.A. Semeniuk writes to us from Canada and is completing her scholarly portfolio towards a PhD in Education. Her research interests are in the early identification of students who may be at risk of failure and in designing learning interventions for students in Kindergarten through Grade Seven. She has 32 years of teaching experience and worked in a specialty role as a Learning Assistance Teacher for 15 of those years. Email: miriamsemeniuk@gmail.com

 

Abstract

The time sequenced (t1, t2, tn), formative assessment model presented here aligns directly with how language is acquired and subject matter is learned. The paper explains how data on learners’ language and content may be documented with the aim to have students at all levels increase in self–regulating their learning—from Kindergarten through Grade 12 and beyond. The end goal in all applications of time sequenced analyses is to have learners attain high levels of academic literacy while demonstrating their command of the spoken and written language used in communicating what they know.

 

Introduction

When assessing progress in language and content acquisition, we assess how a learner develops concepts and the way information is processed through an analysis of the language used orally and in writing. Time sequenced analyses (t1, t2, etc.) is suitable for monitoring the depth of processing of newly introduced concepts and permits a fine–grained analysis of language at various linguistic levels. This approach to formative assessment is adapted from Willem Levelt’s (1989) language acquisition theory (see Guerrero, 2005) and is applied here to second and additional language (L+) learning. The Psycholinguist divided language into four stages of production: (1) conceptualization/message planning, (2) formulation of a linguistic structure involving lexical and syntactic procedures (grammatical encoding), (3) articulation of the message (phonetic plan prepared as internal speech), and (4) self–monitoring what is produced. These four components are iterated as information is processed at deeper levels using new, increasingly complex material.

To better understand this approach to formative assessment, language acquisition and language learning are differentiated here.  Language acquisition is a subconscious, natural process of acquiring a language–typically through exposure and immersion, much like a first language is learned in childhood. In contrast, language learning is a deliberate, conscious process of formally studying a language, focusing on rules and grammar. Whereas acquisition relies on natural communication, learning relies on explicit instruction and is often more structured and slower.

While working on any text for assignments in subject content areas, several drafts are generated. For each version, ideas are reconceptualized, reformulated, newly articulated, with feedback provided on how knowledge is applied using higher order thinking skills (e.g., Bloom et al., 2001: analyzing, evaluating, creating) in completing a final task. Winne (2018) merges the levels (depths) of information processing into a model of self–regulated learning (SRL) (Zimmerman, 2002) in which learners survey potential external and internal resources for learning and identify constraints on their learning (i.e., access to resources, time available, interests/prior knowledge).

In SRL, learning goals are set, plans are formulated about what students hope to achieve, and criteria are determined for the task. As the work unfolds, standards selected are used metacognitively to monitor student progress with feedback provided specific to each learner’s engagement in brief (3–minute), targeted conferences. A student’s self–evaluation may prompt them to adjust their course of action or the product. The final text and information accumulated in the formative assessments are evaluated to produce a summative account of learning as displayed in Figure 1.

 

Figure 1   Levelt’s Language Production Model and Ongoing Formative Assessment
T1 Conceptualizer	T2 Formulator	T3 Production	T4, Tn Increased Complexity, Accuracy, Fluency
L1/L2 Mixed	Formulaic language	Acquired terminology and text structure	Use of more complex text structures Increasingly target–like production Augmented learner autonomy

 

As translanguaging between first and L+ is commonly accepted in the initial survey (t1) of what learners know, and linguistic repertoires are accessed in conceptualizing what will be stated, the use of a student’s home languages and the target language are monitored. The next stage of production involves lexical and syntactic selection from one’s linguistic repertoire in formulating what will be stated. Output is typically formulaic and/or mimics the texts learners are exposed to and speak about.

As language lessons are scaffolded with the aim to improve communicative competence (c f Council of Europe’s 2018 descriptors), an increase in performance would be anticipated in language profiles in any (and all) time sequenced analyses (i.e., production t2, t3, tn). This would include targeted instruction of academic terminology, text structure, more elaborate, enriched language and sentence complexity, with improved fluency and accuracy, all of which provide a framework for curriculum planning (Ortega, 1999; Skehan & Foster, 2012; Tonkyn, 2012). These data on student progress in acquiring the constructs which are taught explicitly are then integrated into assessment conversations held in individual conferences.

Student progress in content learning and language development can be documented on an analytic formative assessment grid such as the one provided below (see Figure 2). Five constructs to include in a fine–grained analysis of oral texts that have been recorded, examined—and possibly transcribed for speech and language interventions—include (1) a word count (mean number of words), (2) the mean length of sentences, (3) the number of different words, (4) sentence complexity, and (5) an error analysis (Burchell & Cleave, 2023).

Last, effective feedback (Hattie & Timperley, 2006, in Hattie, 2012) may guide learners in raising their awareness of language form (metalinguistics) and function (metacognition). Therefore, the way feedback provided on spoken and written texts is integrated in subsequent drafts is appraised to raise student’s awareness of their language use and grammar.

 

Student self–assessment

Reflecting on one’s progress in content and language integrated learning (CLIL) has potential to enhance self–regulated learning. Feedback on texts can be used to prompt students to reflect on language use (metalinguistics) and the processes for comprehending and expressing what is learned (metacognition). Student self–assessment is integral to formative evaluation practices and may be documented over a school year and across grades on a record sheet like the one in

Figure 2.

Effective feedback answers three questions (Hattie & Timperley, 2006) in Hattie (2012):

1. What will I work on?
2. How will I accomplish this?
3. Where to next?

Each of these questions can be applied at four levels:

1. The task: Assess the overall success of the task.
2. The process: Identify which strategies are needed to perform the task?
3. Self–regulation: Monitor progress towards completing the task.
4. Learner autonomy: Reflect on progress made and affect about learning.

 

Figure 2   Analytic assessment grid for content, language, integration of feedback, and self–evaluation
Unit of Analysis	t1	t2	t3	t4
Content information
Content language shows increasing use of academic terminology/form
Communication Skills show an increase in complexity, fluency, and accuracy
Listening Understands increasingly complex information Spoken language Complexity Fluency Accuracy Reading Integrates new ideas in writing Writing Complexity Fluency Accuracy
How feedback is integrated
Evaluation of a learner’s ability to assess their own progress: Metacognition Metalinguistics

The diagnostic information collected this way satisfies Bachman and Palmer’s (2010) criteria for test use. Time sequenced analyses (1) are beneficial to stakeholders; (2) reflect community values and are equitable for stakeholders who are affected by decisions made (i.e., actions taken) based on multiple analyses; (3) interpretations about abilities being assessed are meaningful, relevant, sufficient, impartial, and generalizable to the content and language domains in which instructional decisions are made; and (4) the assessment records (descriptors, scales of reference, scores/scoring process) are consistent across different assessment tasks, different aspects of classroom procedures (e.g., forms, occasions, raters), and across different groups of test takers. This highly interactive, formative approach to assessment in CLIL has potential for bringing all learners to high levels of proficiency and autonomy in learning in all academic areas.

 

References

American Institutes for Research (2021). English Learner oral narrative scale. English Learner Literacy Intervention Program Ensuring Success. https://www.ellasconsulting.com/uploads/9169313145784655354103.pdf

Bachman, L. F., & Palmer, A. S. (2010). Language assessment in practice. Oxford University Press.

Bloom, B., Englehart, M., Furst, E., Hill, W, & Krathwohl, D. (2001). A taxonomy for teaching, learning, and assessment. https://bloomstaxonomy.net/

Burchell, D., & Cleave, P. (2023, March 24). Expanding oral language assessment; Narrative microstructure [Conference session]. Languages Without Borders (LWB) Conference, Toronto, Ontario. Organized by the Canadian Association of Second Language Teachers (CASLT) and the Ontario Modern Language Teachers’ Association (OLMTA).

Council of Europe (2018): Common European framework of reference companion volume with new descriptors, (CERF/CV).

https://www.ecml.at/News/TabId/643/ArtMID/2666/ArticleID/1414/Council-of-Europe-

Launching-Conference-CEFR-Companion-Volume-with-New-Descriptors-CEFRCV.aspx

Guerrero, R. G. (2005). Task complexity and L2 narrative oral production [Unpublished Ph. D. dissertation]. University of Barcelona, Spain. https://www.tdx.cat/bitstream/handle/10803/1662/01.CHAPTER_1.pdf?sequence=2

Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. Routledge.

Ortega, L. (1999). Planning and focus on form in L2 oral performance. Studies in Second Language Acquisition, 21(1), 108‒148. https://doi:10.1017/S0272263199001047

Skehan, P., & Foster, P. (2012). Complexity, accuracy, fluency and lexis in task–based performance: A synthesis of the Ealing research. In A. Housen, I. Vedder, & F. Kuiken (Eds.), Dimensions of L2 performance and proficiency: Complexity, accuracy, and fluency in SLA (Vol. 32, pp. 199‒ 220) [eBook edition]. John Benjamins Publishing Company.

Tonkyn, A. (2012). Measuring and perceiving changes in oral complexity, accuracy and fluency: Examining instructed learners’ short–term gains. In A. Housen, I. Vedder, and F. Kuiken (Eds.), Dimensions of L2 performance and proficiency: Complexity, accuracy, and fluency in SLA (Vol. 32, pp. 221‒ 245) [eBook Version]. John Benjamins Publishing Company.

Winne, P. H. (2018). Theorizing and researching levels of processing in self–regulated learning. British Journal of Educational Psychology, 88(1), 9–20. https://doi.org/10.1111/bjep.12173

Zimmerman, B. J. (2002). Becoming a self–regulated learner: An overview. Theory into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

 

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