Integrated Language and Science & Technology Instruction: A Cognitive Task Analysis of the Required Teacher Expertise

Figures & data

Table 1. Contextual characteristics of teachers’ ILS&T lessons.

Name*	Grade(s) taught**	Number of students	Student characteristics	Learning goals
1. Christie	4	25	Low-SES, low language proficiency	Experience sound as a vibration. Understand the difference between high and low sounds and loud and soft sounds through experimentation. Develop domain-specific vocabulary.
2. Alice	Kinder-garten	23	Low-SES	Understand how mirrors work and what a reflection is. Develop oral language skills (conversations).
3. Sophia	Kinder-garten	16	Bilingual, low and high SES	Design an insect hotel. Develop domain-specific vocabulary. Develop oral language skills (conversations).
4. Janet	4, 5, 6	10	Bilingual, low language proficiency	Understand reasoning pattern pro-con. Create a design that solves a problem. Construct sentences using signal words and appropriate grammar. Develop domain-specific vocabulary.
5. Elisabeth	5, 6	10	Multi-lingual	Present design ideas. Understand the function and structure of the narrative genre.
6. Nicole	1	14	Low and high SES, bilingual	Experience what air pressure is and how it works through experimentation. Develop domain-specific vocabulary.
7. Mandy	6	22	No special characteristics	Understand what communication and noise are. Develop oral language skills (conversations, formulating conclusion). Develop reading comprehension skills.
8. Nadine	5, 6	16	No special characteristics	Understand how a bicycle chain transmits force through experimentation. Develop domain-specific vocabulary.
9. Kayleigh	2, 3, 4	44	No special characteristics	Understand the concept of condensation through experimentation. Distinguishing nouns, adjectives, and verbs. Develop reading comprehension skills.

*Names are pseudonyms.

**Grade equivalent in US school system.

Table 2. Implementation of CTA procedures.

CTA procedure (Clark et al., 2008)	Research activity
1. Collect preliminary knowledge	A literature study was carried out to review the characteristics and teaching demands of ILS&T instruction.
2. Identify knowledge representations	In this step, a particular method is chosen which determines how the types of skills and knowledge required to perform the complex task will be organized and represented (in step 5). Based on the Four-Component Instructional Design (4C/ID) model by van Merriënboer and Kirschner (2017), the knowledge representations are formatted in (a) a skills hierarchy that describes the prerequisite relation of the overall learning goal (i.e., teaching ILS&T) to more specific constituent skills, (b) the underlying body of knowledge and (c) the factors influencing task complexity.
3. Apply focused knowledge elicitation methods	Experienced teachers were observed while teaching an ILS&T lesson, followed by semi-structured interviews using stimulated recall.
4. Analyze and verify the data acquired	The data from the observations and interviews were qualitatively analyzed. Two expert meetings were organized with (a) teachers and (b) domain experts, to verify and expand the initial data.
5. Format the results for the intended application	In a follow-up study, the results will be used to design a 4C/ID-based PD program for in-service teachers.

Figure 1. Skills hierarchy for integrated language and science & technology instruction.

Figure 1 shows the skills hierarchy for integrated language and science & technology instruction. It includes the knowledge components as well as the constituent skills that were identified in the cognitive task analysis.

Clark, R., Feldon, D., Van Merriënboer, J. J. G., Yates, K., & Early, S. (2008). Cognitive task analysis. In J. M. Spector, M. D. Merril, J. J. G. Van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed. pp. 577–593). Macmillan/Gale.

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van Merriënboer, J. J., & Kirschner, P. A. (2017). Ten steps to complex learning: A systematic approach to four-component instructional design. Routledge.

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Data availability statement

The datasets generated and analyzed during the current study are available in the Dans EASY repository: https://doi.org/10.17026/dans-zm3-xm2t.