University of Groningen
Improving language use in early elementary science lessons by using a video feedback
intervention for teachers
van Dijk, Marijn; Menninga, Astrid; Steenbeek, Henderien; van Geert, Paul
Published in:Educational Research and Evaluation DOI:
10.1080/13803611.2020.1734472
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date: 2020
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
van Dijk, M., Menninga, A., Steenbeek, H., & van Geert, P. (2020). Improving language use in early
elementary science lessons by using a video feedback intervention for teachers. Educational Research and Evaluation, 25(5-6), 299-322. https://doi.org/10.1080/13803611.2020.1734472
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=nere20
Educational Research and Evaluation
An International Journal on Theory and Practice
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/nere20
Improving language use in early elementary
science lessons by using a video feedback
intervention for teachers
Marijn van Dijk , Astrid Menninga , Henderien Steenbeek & Paul van Geert
To cite this article: Marijn van Dijk , Astrid Menninga , Henderien Steenbeek & Paul van Geert (2019) Improving language use in early elementary science lessons by using a video feedback intervention for teachers, Educational Research and Evaluation, 25:5-6, 299-322, DOI: 10.1080/13803611.2020.1734472
To link to this article: https://doi.org/10.1080/13803611.2020.1734472
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
Published online: 10 Mar 2020.
Submit your article to this journal
Article views: 520
View related articles
Improving language use in early elementary science
lessons by using a video feedback intervention for
teachers
Marijn van Dijk , Astrid Menninga, Henderien Steenbeek and Paul van Geert Heymans Institute for Psychological Research, University of Groningen, Groningen, the Netherlands
ABSTRACT
We developed a teacher professionalisation intervention, called “Language as a Tool for Learning Science”, that focuses on language use during early elementary science lessons, based on video feedback coaching. The aim of this study was to investigate possible changes in teacher student behaviour during this intervention, by analysing teacher-student language during science lessons. Seventeen teachers participated with small teaching groups of 4 –6-year-old students. All task-related communication was coded for teacher questions, teacher language, student language, and reasoning skills. The results show that teachers in the intervention group increasingly used open-ended questions, and students used more utterances related to reasoning. The language of teachers in the intervention group also increased in complexity and sophistication. The students’ language also increased in complexity, but also in the control group. These findings offer insights into effective forms of professional development for teachers in early elementary education.
ARTICLE HISTORY Received 27 August 2019 Accepted 18 February 2020 KEYWORDS
Teacher professionalization; video feedback coaching; language use; science lessons; early elementary education; classroom observations
Introduction
The role of science in early elementary education is becoming increasingly pro-minent (e.g., Greenfield et al.,2009). However, early elementary teachers often see science as a content area beyond the scope of their knowledge and feel great pressure to prioritise language and literacy learning in these early school years (Greenfield et al., 2009). Teachers are often unaware of the importance and possibilities of integrating language and science learning in their classroom (Wong Fillmore & Snow,2002). The language that is required in school settings– also referred to as“academic language” – differs from daily, informal language in that it involves more abstract, complex, and challenging vocabulary and linguis-tic structures (Bailey,2007; Cummins,1980,1981; Scarcella,2003; Zwiers,2008).
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
CONTACT Marijn van Dijk m.w.g.van.dijk@rug.nl 2019, VOL. 25, NOS. 5–6, 299–322
This type of language is the tool students need in order to access content such as understanding science concepts (Nagy & Townsend,2012). In addition, partici-pation in science lessons demands sophisticated science discourse, which entails domain-specific vocabulary, dense presentation of information, and complex sentence structures (Halliday, 1993; Schleppegrell, 2001, 2004; Snow, 2014). In the early years of elementary school, students do not yet master these sophisticated language skills, while expressing complex thoughts with limited language skills is a great challenge (Snow & Uccelli, 2009). Science lessons provide an appropriate context for expanding these necessary language skills giving students the chance to become proficient in using sophisticated words and complex syntax to express ideas (French, 2004; French & Peterson, 2009; Gelman & Brenneman, 2004; Snow, 2014). Several science interventions have indicated significant gains in students’ vocabulary, for instance, in the use of more sophisticated terms (French, 2004; Henrichs & Leseman, 2014; Hong & Diamond,2012). Other research has shown positive changes in students’ syntactic complexity after an intervention directed at improving teacher ques-tioning skills (Lee, Kinzie, & Whittaker, 2012). Thesefindings show that science lessons can serve as a highly suitable context for language learning in thefirst years of primary school and that there may be room for improvement in the teaching practice.
A central question is how teachers can enhance their science lessons in order to stimulate language learning. Several studies have indicated that the quality of the teacher–student interaction is a crucial factor for students’ learning pro-cesses and their general academic performances (Darling-Hammond, 2000; Downer, Sabol, & Hamre,2010; Geringer, 2003; Hattie, 2009). This also applies to the context of language learning, as social interaction has been found to be essential for students’ language development (Dickinson & Porsche, 2011; Powell, Diamond, Burchinal, & Koehler,2010). Therefore, improving the quality of such interaction is an important means for enhancing students’ learning (Barber & Mourshed,2007). The best way to realise this is to enter the context of a teacher’s actual teaching practice and to observe and reflect – together with the teacher – upon the teacher’s behaviour in real-time interactions with students (e.g., Domitrovitch, Gest, Gill, Jones, & Sanford DeRousie,2009; Seidel, Stürmer, Blomberg, Kobarg, & Schwindt,2011).
We developed an intervention called “Language as a Tool for Learning Science” (LaT), which is a teacher professionalisation training that focuses primar-ily on using and eliciting sophisticated language during early elementary science lessons. The intervention combined the use of questioning strategies (open-ended questions based on the empirical cycle) and language learning strategies (Dejonckheere, Van De Keere, & Mestdagh, 2009; Oliveira, 2009; Snow, 2014). These elements are integrated within a method of individual video feedback coaching. Video feedback is an effective and useful method of self-reflection, which can be implemented successfully in a classroom setting (Noell, Duhon,
Gatti, & Connell, 2002; Seidel et al., 2011). In using video feedback, teachers reflect upon video recordings of their own teaching sessions with a professional coach. During sessions of“shared review”, they receive positive feedback and suggestions for improving their teaching practices (Fabiano et al., 2013; Reinke, Sprick, & Knight,2009). This form of reflection not only creates awareness of the teacher’s own behaviour but also of the associated reactions of students (Strathie, Strathie, & Kennedy,2011; van den Heijkant et al.,2006). The effect of video feedback on teacher–student interactions and instructional quality has been demonstrated in several studies (see Fukkink, Trienekens, & Kramer,2011). The LaT intervention is an extension of a previous intervention for teachers called“Curious Minds in the Classroom” (CMC; Wetzels,2015). The CMC interven-tion was aimed at changing teachers’ behaviour, such as their questioning strat-egies, by using video feedback coaching. The underlying idea was that science learning can only take place if students are challenged to think and talk about those activities (Lutz, Guthrie, & Davis,2006; van Keulen & Sol,2012). Structured guidance is essential because it is tempting for students to only actively explore the materials (hands-on) instead of thinking and talking about those materials in order to come to a deeper understanding (minds-on) (Henrichs & Leseman, 2014). In order to organise the thinking process of students and turn a hands-on activity into a rich learning experience, teachers in the CMC interventihands-on were taught to arrange their science lessons according to the empirical cycle (de Groot,1994; Dejonckheere et al.,2009). Observing, predicting, and explaining are regarded as central to the empirical cycle (de Groot,1994) and can therefore be used to measure reasoning by students. Predictions and explanations are posi-tively related to cognitive performance (Christie, Tolmie, Thurston, Howe, & Topping, 2009; Greenfield et al., 2009; Thurston, Christie, Howe, Tolmie, & Topping, 2008). The CMC intervention focused on using open-ended questions in order to stimulate active student participation and challenge students’ thinking. Open-ended questions are found to be the most effective type of question for sti-mulating students to talk and for eliciting complex expressions of reasoning (see Chin,2006; Massey, Pence, Justice, & Bowles,2008; Oliveira,2010). However, the thoughts and ideas of these young students are constrained by verbal demands, which limits the scope of these students’ knowledge and their flexibility in applying– and verbalising – it. For instance, predictions and explanations are linguistically more challenging – compared to descriptions of observations – because they require the expression of relations between objects or events.
Evaluation of the CMC intervention in a group offive teachers revealed mod-erate to large increases in the number of teacher questions that were open-ended and in the students’ reasoning levels associated with the intervention (Wetzels,2015). Although this intervention did not target language learning at all, a reanalysis of the data revealed that the CMC programme also yielded inter-esting changes in language productivity and language use with large effects over time (Menninga, van Dijk, Wetzels, Steenbeek, & van Geert, 2017). Aside
from a change in the balance between teacher and student (students became more productive over time), there was an increase in lexical sophistication and the use of causal connectives by the students. However, no change was observed in the quality of the language of the teachers. For this reason, we argued that should the science lessons also function as language learning activi-ties, explicit attention should be paid to using and eliciting academic language by the teachers. Since this explicit focus on language learning is essential no matter the subject (Wong Fillmore & Snow,2002), the intervention should also explicitly focus on language learning during science activities.
Afirst step towards improving the quality of science lessons is to transform the active exploration of students into content- and language-learning opportunities. It is particularly evident that open-ended questions also tend to elicit more complex student responses, and responses that employ varied vocabulary (de Rivera, Giralometto, Greenberg, & Weitzman,2005; Lee & Kinzie,2012; Mashburn et al.,2008; Wasik & Bond,2001; Wasik, Bond, & Hindman,2006). It is important for teachers to carefully listen to students’ responses, after which adequate follow-up questions (such as “Why do you think that will happen?”) should be posed. This provides opportunities to deepen the students’ understanding and for them to use elaborate language (Chin,2006; Lee & Kinzie,2012). In addition, teachers can provide linguistic scaffolding for students by gradually using more academic language that is nevertheless adapted to their abilities, thereby giving students access to new and potentially more complex and sophisticated language (Bradley & Reinking,2011; Gibbons,2002). This puts a high demand on teachers’ sensitivity and responsiveness to what their students can and cannot do. Effective linguistic scaffolding includes (a) introducing new vocabulary or linguistic struc-tures at the point of communicative need, (b) modelling language that is slightly above the level of students, (c) repeating or rephrasing a student’s utterance in a more sophisticated and complex way, and (d) prompting for more elaboration or specification (Gibbons,2002; Mercer,2000; Yifat & Zadunaisky-Ehrlich,2008).
The aim of the current study was to evaluate the LaT intervention by investi-gating the changes in language use of both teachers and students; the use of questions by teachers; and the students’ use of observations, predictions, and explanations. The results from the CMC intervention suggested that science learn-ing and language learnlearn-ing of students can co-occur to a certain degree, even without explicitly focusing on language learning during the science activities. However, the question is to what extent the new LaT intervention– with its expli-cit attention to language learning opportunities– leads to increased use of open-ended questions by the teacher, by increased use of reasoning expressions of stu-dents, as well as improved use of academic language by teachers and students.
The following research questions were addressed:
(1) To what degree does the use of open-ended teacher questions change during the intervention?
(2) To what degree does the use of utterances with observations, predictions, and explanations by students change during the intervention?
(3) To what degree does the use of syntactically complex sentences and levels of lexical sophistication of teachers’ and students’ language change during the intervention?
(4) To what degree does the use of syntactically complex predictions and expla-nations of students change during the intervention?
We hypothesised that teachers who received the LaT intervention would show increases in the percentages of open-ended questions, complex sentences, and lexical sophistication. Under the premise that changes in students’ verbal expressions can be brought about by working on a teacher’s verbal skills, we expected increases in the percentages of reasoning expressions, complex sen-tences, and lexical sophistication.
Method
Participants
Seventeen experienced female teachers participated in this study, of whom 11 teachers participated in the intervention group and six in the control group. Since all participants had signed up on a voluntary basis, the control group was in fact a waiting list, which ensures that there were no systematic differences in motivation to change before the start of the intervention. At the start of the data collection, the average age of teachers in the intervention group was 40 (range 27–60) with an average experience as a teacher of 14 years (range 3–33). In the control group, the average age of the teachers was 47 years (range 34–59) with an average of 19 years of teaching experience (range 2–38). None of the teachers had participated in the CMC intervention as reported in Menninga, van Dijk, Wetzels, et al. (2017). The teachers selected three to six of their students, varying in age, gender, and cognitive level. All students were 4 to 6 years old (modus 5 years), and they were more or less evenly distributed according to gender. None of the participating students had any notable devel-opmental problems, and all teachers and students were native speakers of Dutch. The teachers and parents of the participating students gave informed consent before the start of the study, and all procedures conformed to existing ethical guidelines. This study was approved by the local Ethical Committee Psychology of the University of Groningen.
Material and measures
The videotaped lessons were transcribed following the Codes for Human Analy-sis of Transcripts (CHAT) conventions (MacWhinney,2000). Partly and completely
unintelligible utterances were excluded from analysis. No distinction was made between contributions of individual students, such that the speaker was ident-ified as either a teacher or a student. This means that the analyses were on the basis of students as a group. The transcripts were exported to Excel, and all off-task utterances, such as “Teacher, can I go to the toilet?” or “Please go to your seat”, were removed before coding. Teacher utterances were coded as (0) no question, (1) closed-ended question, or (2) open-ended question. The coding protocol was based on the coding scheme of de Rivera et al. (2005). Table 1provides an overview of the codes with corresponding examples. After coding each utterance, the percentage of closed-ended and open-ended ques-tions was computed by dividing these frequencies by the total number of teacher utterances (including the 0 codes).
The students’ utterances were coded by the use of skills from the empirical cycle: (1) observation, (2) prediction, (3) explanation, and (0) none of these. Table 2 provides an overview of the categories with examples. Observations (1) require the identification of salient characteristics of an object or event and can include perceptual or abstract relations. These observations are an important starting point for moving to more complex forms of reasoning. Predictions (2) require a mental representation of possible events that may happen in the future but have not happened yet. Explanations (3) result from deductive
Table 1.Coding scheme for questions used by teachers including examples.
Code Type of Expression Example
No Question (0) Utterance related to instructions and information. Please put the marble on the track. Keep your eyes on the table. Gravity is [… ].
Closed-ended question (1)
Question that constrains the student’s response because there is only one correct or acceptable answer, such as yes/no questions, multiple-choice questions, naming questions, et cetera.
Does itfloat?
Will the rice also dance, or do you think it will stay at the bottom of the glass?
So you say it will… ? What is this called? Open-ended
question (2)
Question that does not constrain the student’s response because a number of different answers is acceptable (not one correct answer), such as open-ended questions, thought-provoking questions, elaborations questions, et cetera.
What just happened with the oil? What do you think will happen when
[… ]?
Why do you think that [… ]? How does that work then? Note: Examples originate from the Dutch transcripts and are literal translations into English.
Table 2.Coding scheme for reasoning of students including examples.
Code Type of expression Example
None (0) Task-related utterance, no reasoning. To breathe (answer to: Why do we need air?).
Observation (1) Student describes what he/she observes (sees, hears, feels, tastes, smells).
The marble sinks.
Prediction (2) Student predicts what will/might happen in the future. I think the balloon will make this sound [pffff].
Explanation (3) Student gives reasons in order to explain phenomena. Because it is so heavy. Note: Examples originate from the Dutch transcripts and are literal translations into English.
thinking about an outcome and demand causal relations in order to conclude about the underlying principles of a phenomenon. After coding each utterance, the percentage of observations, predictions, and explanations was computed by dividing these frequencies by the total number of student utterances (including the 0 codes).
Whereas in Menninga, van Dijk, Wetzels, et al. (2017), we were interested in global measures of language production, the current study aimed at conducting a more in-depth analysis of language use. For this reason, each utterance was coded using a system based on the description of sentence complexity provided in Huttenlocher, Vasilyeva, Cymerman, and Levine (2002) and the version of this coding scheme used by Justice, McGinty, Zucker, Cabell, and Piasta (2013). Each utterance was coded for sentence complexity using either (0) the utterance con-tained no clause, (1) the utterance concon-tained one clause (simple sentence), or (2) the utterances contained multiple clauses (complex sentence).Table 3provides an overview of the codes, including examples.
Utterances were based on turn-taking, pauses, and intonation patterns. There-fore, it occurred several times that utterances started with“and”, which usually functions as a coordinate conjunction. Due to the informal character of oral language use, such cases were not coded as complex utterances. Quite often an utterance starting with “and” was not preceded by an utterance of the same speaker, which supported our decision. When “and” was used by the same speaker in the middle of an utterance to coordinate two utterances, then the utterance was coded as a complex one. Incomplete sentences were included among complex sentences if they were a response to, for instance, a teacher question. When the teacher asked “Why does the stone sink?” and a student answered, “Because it is heavy”, the student’s utterance was coded as a complex utterance. In such cases, the teacher’s and student’s utterance together form a complete complex sentence: “The stone sinks, because it is heavy”. It should be noted that in Dutch, the use of “because” (“omdat”) requires a change in word order from subject-verb-object to subject-object-verb, which requires more complex syntactical skills. For instance, the sentence “De steen
Table 3.Coding scheme syntactic complexity including examples of literal translations from the Dutch transcripts.
Teacher Students
No clause (0)
“What about you?” No clause (0)
“Red and blue.”
“Really?” “Yes.”
Simple (1) “What causes sound?” Simple (1) “It floats.”
“And the clay ball sinks to the bottom.” “This marble rolls down very fast.” Complex
(2) “You think that when I hit this very hard thenthe container will fall off the table.”
Complex
(2) “When you open the cap then itfloats but then also the water comes in.”
“We are going to count down because the other time some children let go of the balloon earlier.”
“Well, the air vibrates and that makes noise.”
is zwaar” (“the stone is heavy”) changes into “[de steen zinkt] omdat hij zwaar is” (“[the stone sinks] because it heavy is”). Hence, if the student would have answered“the stone is heavy” (“de steen is zwaar”), this would not have been coded as a complex sentence, because there is no syntactic transformation marking the subordinate-clause transformation in verb-object order required by the word“because” (“omdat”).
After coding each utterance, the percentage of complex utterances was com-puted by dividing the number of complex utterances by the total number of utterances (including the 0 codes).
Lexical sophistication, which is a measure of complex and low-frequency words, was computed for the teachers’ and students’ language use. This was done by means of the measure of lexical richness (MLR). The MLR, developed by van Hout and Vermeer (2007), was calculated with an online tool based on the word list by Schrooten and Vermeer (1994). This word list provides the fre-quency of 26,000 lemmas in a corpus of two million tokens, drawn from oral and written language input in elementary schools in the Netherlands. The corpus contains words from picture books, factual subjects, and mathematics textbooks, but also from oral teacher input during instruction and other inter-actions in the classroom, making it an adequate corpus to use in the context of science lessons. The online tool generates an MLR index, which is a weighted score resulting from the comparison between the frequency distribution of lemmas in the submitted transcript and the distribution of the Schrooten and Vermeer corpus. Although MLR is commonly used to estimate children’s vocabu-lary size (van Hout & Vermeer,2007), in the current study MLR is used for both the students and the teacher as an indicator of lexical sophistication.
The inter-rater reliability for the application of the coding schemes was calcu-lated. The inter-observer agreement was considered substantial (skills from empirical cycle = 78%) to almost perfect (questioning = 88%, sentence complex-ity = 88%) agreement. Cohen’s kappa calculated to determine the consistency of coding among the two observers (Landis & Koch,1977) revealed substantial con-sistency for questioning (κ = .79) and skills from empirical cycle (κ = .61), and almost perfect consistency for sentence complexity (κ = .83).
Procedure
The teachers were recruited from schools in the north of the Netherlands by means offlyers and personalised emails. The teachers in the intervention con-dition were instructed to give eight science lessons (15–20 minutes each), one lesson every week for eight weeks. The choice of the topic was left free in order to support the teachers’ self-efficacy. On request, teachers were given some suggestions for easily accessible topics (e.g., from the website www. proefjes.nl). Most teachers chose subjects such as crafting, designing, floating and sinking, air pressure, senses, sound, and the marble track. The first two
lessons took place before the intervention started (pre-intervention measures). After the second lesson, the teachers received information about the goals of the LaT intervention. An educational meeting was the starting point of the inter-vention as information transfer on new teaching strategies is a powerful ingredi-ent within learning processes. In this meeting, teachers were provided with information and tools on the use of open-ended questioning strategies, the empirical cycle, scaffolding, and language learning strategies. Several video clips from the teachers’ own pre-intervention measures were shown to illustrate the previously given information. After this, the teachers were asked to specify a personal learning goal that was used as a special point of interest for both the teacher and coach in the coaching sessions. An example of a teacher’s personal learning goal was:“I want to ask open-ended questions based on the empirical cycle”. During Lessons 3 to 6, teachers received individual video feedback coach-ing immediately after each science lesson. Durcoach-ing each of these sessions, the coach selected several moments from the lesson, based on a ratio of three moments that showed successful teacher behaviour to one moment that could be improved. Coach and teacher discussed and reflected upon these moments to bring the teacher’s behaviour to a conscious level, guided by the teacher’s personal learning goals. Two to 4 weeks after the final coaching session, teachers gave two further lessons, which were post-intervention measures. The total intervention period, from pre-intervention to post-interven-tion measure, was 3–4 months. The intervention was adaptive in nature as the individual video feedback coaching was adjusted to the personal learning goals of the teachers, and to the particular situations from which the teacher– student interactions emerged. Within this adaptive context, we aim to ensure the quality of the implementation. First, the intervention was performed by an experienced coach (second author of this article), which ensures the quality of the intervention. Second, only participants who completed the intervention were included, which is important with respect to the quantity of the interven-tion. One teacher dropped out after Lesson 5 due to personal medical reasons. Third, the participant responsiveness, which is the degree to which the pro-gramme stimulates the interest of participants, was considered substantial as all teachers invested their own time in preparing the lessons and joining the information meeting, and they all actively participated during the intervention. The teachers in the control condition were given the same instruction as the teachers in the intervention condition in the pre-measurements: teach science “as usual” on a topic of your own choice. On request, these teachers were also given some suggestions for easily accessible topics (e.g., from the websitewww. proefjes.nl). The control group was observed during two pre-intervention measures and approximately 3 months later during two post-intervention measures. The period from pre-intervention to post-intervention measure was 11–18 weeks in the control group. In the period between those measures, the control group did not receive any instructions or feedback. The control teachers
indicated that they taught science on a regular basis (on average once to three times a month, which is comparable to the teachers in de experimental condition).
Analysis
In the analysis, the data of the two pre-intervention lessons were taken together as one average measure, and the same applies to the post-intervention measures. Due to the small sample size (Todman & Dugard, 2001), we used Monte Carlo permutation tests. The analyses were performed in Microsoft Excel in combination with PopTools (Version 3.2). We tested whether the di ffer-ences between the observed data in the pre-intervention measures and post-intervention measures were equal to or smaller than what would be expected if all data came from one single distribution. In order to achieve this, the empiri-cal data were randomly shuffled 10,000 times and compared with the empirically found difference (i.e., between groups or between pre-intervention and post-intervention measures within groups). Monte Carlo permutation tests provide an estimation of the exact p value; the probability that the same or a better di ffer-ence is found if the null hypothesis is true. As significance scores are not directly linked to practical significance (Sullivan & Feinn, 2012), we also computed an effect size (Cohen’s d). Based on Cohen’s classification (1992), an effect size of .20 is small, an effect size of .50 is medium, and an effect size of .80 is large. The same holds for their negative counterparts. Only when the empirically found difference had a very small probability of being produced under the null hypothesis– with a p value smaller than .05 and an effect size larger than .50 (or−.50) – was it interpreted as strong evidence in support of the hypothesis. Empirical results with p values between .05 and .10 and an effect size greater than .50 (or−0.50) were interpreted as weak support.
Results
Prior to evaluating the effects of the intervention, we compared the average pre-intervention measures (thefirst two lessons) of teachers and students between intervention and control condition. There were no differences between con-ditions at the start regarding the percentage of open-ended questions, reason-ing expressions, and complex sentences that could not be explained by the null hypothesis. However, the participants in the control group showed somewhat higher lexical sophistication than those in the intervention group (seeTable 4).
RQ1: open-ended teacher questions
The results show that teachers in the intervention group posed relatively more open-ended questions in the post-intervention lessons (seeTable 4). The prob-ability that the difference in the percentage of these open-ended questions is
based on chance is very low, and the effect is large, which indicates strong support for an increase. In the control group, there was no support for a change over time.Figure 1shows that in the intervention condition this percen-tage increased rapidly after pre-intervention measures, and continued to increase until the last coaching session (Coach 4). At post-intervention, the average percentage of open-ended questions stabilised at a higher level than at pre-intervention measures. The figure also illustrates the differences between the two conditions.
Table 4.Pre- and post-intervention measure comparisons within the intervention group and pre- and post-intervention measure comparisons within the control group.
Intervention Control
pre post p d pre post p d
Open-ended questions Teachers 10% 19% <.01* 1.68 11% 10% .62 −.13 Reasoning
Total Students 25% 34% .02* .70 25% 24% .65 −.16
Predictions Students 6% 9% .03* .56 6% 7% .22 .30 Explanations Students 3% 5% <.01* .80 3% 3% .43 .08 Language use
Syntactic complexity Teachers 22% 26% <.01* .90 22% 22% .45 .06 Students 10% 18% <.01* 1.20 8% 14% <.01* 1.07 Lexical sofistication Teachers 2.57 3.04 .02 .61 2.85 3.12 .32 .25 Students 2.98 3.69 .07 .46 3.55 3.27 .74 .27 Syntactically Complex
reasoning expressions
Students 40% 65% <.01* .83 44% 66% .05* .71 *significant at p < .05.
Figure 1.Average percentages of open-ended teacher questions of all on-task teacher utter-ances in both the intervention and control condition.
RQ2: reasoning expressions of students
A comparison between pre- and post-intervention revealed that there was strong support for changes in the total percentage of reasoning-related utterances (observing, predicting, and explaining) relative to all task-related utterances of students in the intervention condition. There were no indications of change in the control condition (seeTable 4).Figure 2 pro-vides an overview of these average percentages of students’ reasoning expressions. For the intervention condition, we observed a gradual increase in the percentage of reasoning expressions of students until thefinal coach-ing session. After this, there seems to be a slight decrease. At post-interven-tion measure, the difference between intervention and control condition is still clearly present.
The percentage of predictions and explanations increased from pre- to post-intervention. The analyses indicated that there is a very low probability that the differences in the intervention condition were based on chance, with a large effect, indicating strong support for an increase. This probability is high in the control condition (see Table 4), which gives no indication for changes regarding the percentage of predictions and explanations. As shown in Figure 3, the percentage of predictions rapidly increased after the pre-intervention measures within the intervention condition, and the percen-tages of explanations showed a more gradual increase over time. Both predic-tions and explanapredic-tions seem to slightly decrease at second post-intervention measure.
Figure 2.Average proportion of the use of observations, predictions, and explanations in all on-task students’ utterances in both the intervention and control condition.
RQ3: complexity and sophistication of language use
The percentage of syntactically complex sentences relative to all task-related utterances increased for both students and teachers in the intervention con-dition. The probability that these differences between pre-intervention measures and post-intervention measures are based on chance is very low, and the effect is large (seeTable 4), which provides strong support for the hypothesised increase. Students in the control condition also showed an increase in the percentage of complex sentences, for which we found strong evidence. There was no indi-cation of change in the control group for the teachers’ percentages of complex sentences (see Table 4). As can be seen inFigure 4, the language of the students in both conditions increased in the percentage of complex sen-tences. At post-intervention measure, the language of the students in the inter-vention condition showed higher percentages than that of the students in the control condition. The language of the teachers in the intervention condition seems to increase gradually until the last coaching session, and to slightly decrease at thefirst post-intervention measure.
Figure 3.Average proportion of predictions (left) and explanations (right) in all on-task students’ utterances in the intervention condition and the control condition.
Note: Error bars indicate the variation between individuals based on standard deviations.
Figure 4.Average proportion of complex sentences in all on-task utterances expressed by tea-chers (left) and students (right) in the intervention condition and the control condition.
With regard to lexical sophistication, the language use of students and teachers in the intervention condition increased. The probability that the difference between the pre- and post-intervention measures is based on chance alone is very low for the teachers, providing strong support for this increase. For the students, we found weak support for an increase in lexical sophistication. In the control con-dition, there were no indications of changes in lexical sophistication of either the students or the teachers (seeTable 4). We observed that, initially, students and tea-chers in the control conditions expressed higher levels of lexical sophistication (see Figure 5). The development of students’ sophistication over time within the inter-vention condition showed an increase over time, but the pattern can be character-ised by some session-to-session variability. For the teachers, on average, there was a slight gradual increase after pre-intervention measures.
RQ4: syntactic complexity of reasoning expressions
The final research question focused on the syntactic complexity of students’ reasoning expressions (predictions and explanations). When comparing pre-and post-intervention measures, there was strong evidence indicating that the expressions of reasoning increased in syntactic complexity in both conditions (see Table 4). Figure 6 depicts the gradual increase in syntactic complexity of reasoning expressions for the intervention group. The graph shows that the control group varies somewhat more between sessions, and that the percentage of complex reasoning expressions of the second pre-intervention measure (54%) and the second post-intervention measure (58%) are close to one another.
Discussion
First of all, thefinding that the teachers’ questioning strategies changed after the intervention is in line with previous studies on teacher training in classroom
Figure 5.Average lexical sophistication for language use expressed by teachers (left) and stu-dents (right) in the intervention condition and the control condition.
settings (Beck, McKeown, Sandora, Kucan, & Worthy, 1996; Wasik et al., 2006; Wolf, Mieras, & Carey, 1996) and also in the specific context of elementary science activities (Lee et al.,2012; Wetzels, 2015). This increase in open-ended questions shows that the teachers made use of questioning strategies in their teaching practice, which may have contributed to a shift in teacher–student interaction from a mostly teacher-dominated style – giving information and instructions – to a way of teaching in which students actively participate in lessons, as we had observed in Menninga, van Dijk, Wetzels, et al. (2017). In addition, the LaT intervention had an explicit focus on academic language of tea-chers in the context of science lessons, which was not the case in the earlier CMC study, and the results showed that the teachers who participated in LaT showed increased syntactic complexity and lexical sophistication in their language. This confirms the possibilities of combining science and language learning in early elementary classrooms (Conezio & French, 2002; French,2004; French & Peter-son,2009; Gelman & Brenneman,2004; Snow,2014; Spycher,2009; Wellington & Osborne,2001).
Second, the results showed that students increasingly used reasoning expressions during the science lessons. The increase in the students’ use of observations, predictions, and explanations might be linked to the change in tea-chers’ questioning strategies, since open-ended questions tailored to the abilities of students tend to elicit more reasoning (Chin,2006; Newton & Newton,2000;
Figure 6. Average percentages predictions and explanations that contained a syntactically complex expression for students in the intervention group and students in the control group.
Oliveira, 2010). The structured guidance, following the steps of the empirical cycle, offered a means to organise and improve reasoning skills of students (Dejonckheere et al.,2009; Gelman & Brenneman,2004; Greenfield et al.,2009; Klahr,2000). Although students’ use of skills from the empirical cycle increased over the course of the intervention, it should be noted that the percentages after the intervention were still quite low: 3–5% for explanations and 6–9% for predic-tions. Clearly, the majority of reasoning expressions of these young students are of the category of observations. However, these low percentages say nothing about the actual functional significance of the use of explanations and predic-tions for children’s reasoning skills. Also, it has been argued that working accord-ing to the empirical cycle tends to have a more long-term, indirect effect on students’ scientific knowledge (Dejonckheere et al.,2009).
Third, the language of students increased in syntactic complexity, and more complex sentences were used to express predictions and explanations. However, it is important to note that students in the control condition also showed increases in both of these variables. Although the effect sizes are slightly lower than in the intervention group, it is not possible to attribute the changes in the student language directly to the elements of the LaT intervention. It may also be explained by the natural linguistic development of early elementary students or by the exposure to science content. The students’ increase in lexical sophisti-cation was also smaller than expected, based on previous studies that focused on improving vocabulary in science activities. However, in most of these studies the focus was on explicit learning of science vocabulary (French, 2004; Henrichs & Leseman,2014; Hong & Diamond,2012). It may be speculated that explicit word-learning strategies immediately become visible in the students’ language use, while more implicit or deductive language-learning strategies – which were the focus of the current intervention – may have a more long-term effect on students’ language development.
Since the central aim of the LaT intervention was to integrate science and language learning, the question may be asked as to whether the increased use of open-ended questioning strategies and language modelling strategies of the teacher leads to students’ use of complex sentences. This idea would be in line with previous studies on teachers’ open-ended questioning and language input and the associated positive outcomes for students’ language (de Rivera et al.,2005; Lee & Kinzie,2012; Massey et al.,2008; Oliveira, 2010; Wasik et al., 2006). It also would follow from results from a previous analysis on only the pre-intervention measures of the current data set (Menninga, van Dijk, Steen-beek, & van Geert, 2017). In this earlier study, an utterance-by-utterance sequence analysis showed that syntactically complex teacher utterances were immediately followed by syntactically complex student utterances significantly more often than based on chance, and the effect was large (d = 0.79 for experi-enced teachers and d = 0.90 for novices). In addition, open-ended teacher ques-tions were significantly more often followed by a complex sentence than based
on chance, with a large effect size (d = 1.32 for experienced teachers and d = 1.78 for novices). This means that within science lessons, in a context without any specific intervention, complex teacher language and open-ended questions do elicit complex student language. However, the currentfinding that the control group showed marked changes in student language indicates that increasing the use of these teaching strategies is not the only way to stimulate student language. Such an improvement can also be caused by the repeated exposure to the science content, the familiarity with the lesson structure, students in fluen-cing each other, and many other factors. More generally, although the results
have shown changes in teacher behaviour and students’ reasoning and
language use, it is impossible to pinpoint any specific causal relation between variables or to attribute the changes to any specific element of the LaT interven-tion (e.g., the video coaching, the language strategies, the open-ended ques-tions, the empirical cycle, the science content). This is not due to weaknesses in the design; rather, it is due to the complexity of the phenomenon, where the causality runs in complex networks of interacting components. The most robust effect we observed in this study is that the LaT intervention changed the language use of teachers, which was not the case in the earlier CMC interven-tion. How exactly this leads to language improvement in students, however, seems to be neither linear nor direct. The causal mechanisms that create the changes at the end of the line are due to a complex interplay between a multi-tude of factors, however, in which not one thing can be singled out.
There are some clear limitations with regard to the design of the study. First, the small number of participants in the current study clearly limits the generali-sability of the results, as was also the case for the CMC study (Menninga, van Dijk, Wetzels, et al.,2017). However, afirst step towards evaluating the effectiveness of an intervention is to perform the intervention on highly motivated participants (Ryan & Deci,2000). Using the motivation of teachers as a starting point and enhancing teachers’ self-efficacy during the intervention were important pre-mises of the LaT intervention. The next step, however, is to perform this interven-tion on larger and more representative groups of teachers. Since effects can only be reliably studied in in-depth studies, the small number is often a direct conse-quence of this necessarily labour-intensive approach. Much greater samples that, for pragmatic reasons, involve more superficial analyses of the variables of inter-est will not contribute to the relevance and validity of the obtained results. The problem of validity can only be solved by connected series of studies which can lead to meta-analyses of studies based on valid methodologies.
Second, it is important to note that the lessons took place in small teaching groups compared to the whole-classroom setting. Small-group activities, which were the focus of the current study, are increasingly becoming part of the everyday educational practice because they allow for more intensive inter-action. In whole-classroom settings, there are fewer opportunities for deepen-ing understanddeepen-ing and askdeepen-ing follow-up questions, let alone takdeepen-ing into
account the abilities of each individual student. In traditional teaching settings in early elementary classrooms, teachers are to a large extent concerned with classroom management and giving instructions (Wolff, van den Bogert, Jar-odzka, & Boshuizen, 2015). For the implementation of educational interven-tions, such as the LaT, it is recommended to start off with small teaching groups. An interesting direction for future research is to explore whether and how teachers apply these principles to a whole-classroom setting in early elementary science education.
Another limitation is that there is no way of knowing whether the positive effects on teachers and students are strong enough to make a lasting impression. Unfortunately, positive outcomes of an intervention tend to disappear relatively soon after the intervention hasfinished (Han & Weiss,2005). The natural class-room setting in which the intervention took place, however, offers good oppor-tunities for teachers to continue to apply and expand the skills they learned in their daily teaching routines. This study, as most experimental studies, has focused on short-term detectable changes, and not on long-term complex and interwoven changes. However, the problem with long-term changes is that it becomes increasingly difficult to pinpoint a particular identifiable cause. Identifi-able causes, such as a particular intervention, are almost by definition linked with short-term easily observable effects, which are then often projected into the future. This applies to actual effects as well as effects that lack statistical signifi-cance in short-term comparisons.
Afinal – more fundamental – limitation of the current study is the impossi-bility to disentangle the causal elements of the intervention, as we mentioned before. This specific combination of elements (the science lessons, the question-ing strategies, the language strategies, the empirical cycle, the video interaction component, this specific number of coaching sessions) and this specific context of early education has shown interesting changes in teacher and student behav-iour. For future studies, it may be considered to expand the number of coaching sessions and tofirst start with the open-ended questioning strategies and later introducing the language strategies. This would make it possible to study the respective changes in behaviour in a somewhat more controlled manner. Although it would not really solve the causality issue, this might increase the plausibility of the effectiveness of interventions with regard to achieving edu-cational goals, such as an increase in syntactic complexity or explanations of scientific phenomena.
Thefindings of this study have implications for teachers and school directors, as they often feel they have to prioritise language teaching over science teaching (e.g., Greenfield et al.,2009). The results support the current movement advocat-ing the replacement of subject-oriented teachadvocat-ing with cross-curricular teachadvocat-ing, which is regarded as essential for fulfilling the demands of (the skills of) the 21st century (Saavedra & Opfer,2012). It also illustrates that science and language learning can be integrated in early elementary grades. Nevertheless, we
acknowledge that this integration is challenging for teachers, and that is why professional guidance is required.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by Platform Beta Techniek, the Netherlands.
Notes on contributors
Marijn van Dijkis a developmental psychologist and works as a professor in the expertise group Developmental Psychology at the Heymans Institute for Psychological Research, Uni-versity of Groningen. Her research focuses on the dynamics of development and learning in early primary education. She is an expert of the observation of interaction behaviours in naturalistic circumstances.
Astrid Menningais a neurolinguist and researcher. In 2017, she received her PhD on the dis-sertation Language and Science in Young Learners at the University of Groningen, of which this work is part. She currently works at OCRN, a mental healthcare institution for children.
Henderien Steenbeek is a developmental psychologist and works as lector Learning and Behavior at the Hanze University for Applied Studies in Groningen. In addition, she holds a position as associate professor in the expertise group Developmental Psychology at the Heymans Institute for Psychological Research, University of Groningen. Dr Steenbeek is an expert in applying the complexity approach to learning/teaching interaction processes.
Paul van Geertis emeritus professor of Developmental Psychology at the University of Gronin-gen. He is renowned for the application of the framework of complex dynamic systems to a wide variety of phenomena with regard to learning and development.
ORCID
Marijn van Dijk http://orcid.org/0000-0002-2823-1455
References
Bailey, A. L. (Ed.). (2007). The language demands of school: Putting academic English to the test. New Haven, CT: Yale University Press.
Barber, M., & Mourshed, M. (2007). How the world’s best-performing school systems come out on top. London: McKinsey.
Beck, I. L., McKeown, M. G., Sandora, C., Kucan, L., & Worthy, J. (1996). Questioning the author: A yearlong classroom implementation to engage students with text. The Elementary School Journal, 96(4), 385–414.doi:10.1086/461835
Bradley, B. A., & Reinking, D. (2011). A formative experiment to enhance teacher-child language interactions in a preschool classroom. Journal of Early Childhood Literacy, 11(3), 362–401.doi:10.1177/1468798411410802
Chin, C. (2006). Classroom interaction in science: Teacher questioning and feedback to stu-dents’ responses. International Journal of Science Education, 28(11), 1315–1346. doi:10. 1080/09500690600621100
Christie, D., Tolmie, A., Thurston, A., Howe, C., & Topping, K. (2009). Supporting group work in Scottish primary classrooms: Improving the quality of collaborative dialogue. Cambridge Journal of Education, 39(1), 141–156.doi:10.1080/03057640802702000
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155–159. doi:10.1037/0033-2909.112.1.15
Conezio, K., & French, L. (2002). Science in the preschool classroom: Capitalizing on children’s fascination with the everyday world to foster language and literacy development. Young Children, 57(5), 12–18.
Cummins, J. (1980). The construct of language proficiency in bilingual education. In J. E. Alatis (Ed.), Current issues in bilingual education (pp. 81–103). Washington, DC: Georgetown University Press.
Cummins, J. (1981). The role of primary language development in promoting educational success for language minority students. In California State Department of Education (Eds.), Schooling and language minority students: A theoretical framework (pp. 3–49). Los Angeles, CA: Evaluation, Dissemination, and Assessment Center, California State University, Los Angeles.doi:10.13140/2.1.1334.9449
Darling-Hammond, L. (2000). Teacher quality and student achievement. Education Policy Analysis Archives, 8, 1.doi:10.14507/epaa.v8n1.2000
de Groot, A. D. (1994). Methodologie: Grondslagen van onderzoek en denken in de gedragswe-tenschappen [Methodology: Foundations of research and thinking in the behavioural sciences].‘s Gravenhage: Mouton and Co.
Dejonckheere, P. J. N., Van De Keere, K., & Mestdagh, N. (2009). Training the scientific thinking circle in pre- and primary school children. The Journal of Educational Research, 103(1), 1–16.
doi:10.1080/00220670903228595
de Rivera, C., Girolametto, L., Greenberg, J., & Weitzman, E. (2005). Children’s responses to edu-cators’ questions in day care play groups. American Journal of Speech-Language Pathology, 14(1), 14–26.doi:10.1044/1058-0360(2005/004)
Dickinson, D. K., & Porsche, M. V. (2011). Relation between language experiences in preschool classrooms and children’s kindergarten and fourth-grade language and reading abilities. Child Development, 82(3), 870–886.doi:10.1111/j.1467-8624.2011.01576.x
Domitrovich, C. E., Gest, S. D., Gill, S., Jones, D., & Sanford DeRousie, R. (2009). Individual factors associated with professional development training outcomes of the Head Start REDI program. Early Education and Development, 20(3), 402–430. doi:10.1080/ 10409280802680854
Downer, J., Sabol, T. J., & Hamre, B. (2010). Teacher–child interactions in the classroom: Toward a theory of within- and cross-domain links to children’s developmental outcomes. Early Education and Development, 21(5), 699–723.doi:10.1080/10409289.2010.497453
Fabiano, G. A., Vujnovic, R. K., Waschbusch, D. A., Yu, J., Mashtare, T., Pariseau, M. E.,… Smalls, K. J. (2013). A comparison of workshop training versus intensive, experiential training for improving behavior support skills in early educators. Early Childhood Research Quarterly, 28(2), 450–460.doi:10.1016/j.ecresq.2012.12.001
French, L. (2004). Science as the center of a coherent, integrated early childhood curriculum. Early Childhood Research Quarterly, 19(1), 138–149.doi:10.1016/j.ecresq.2004.01.004
French, L., & Peterson, S. (2009). Learning language through preschool science. In C. Andersen, N. Scheuer, M. Pérez Echeverría, & E. V. Teubal (Eds.), Representational systems and practices as learning tools (pp. 77–92). Rotterdam: Sense.
Fukkink, R. G., Trienekens, N., & Kramer, L. J. C. (2011). Video feedback in education and train-ing: Putting learning in the picture. Educational Psychology Review, 23(1), 45–63. doi:10. 1007/s10648-010-9144-5
Gelman, R., & Brenneman, K. (2004). Science learning pathways for young children. Early Childhood Research Quarterly, 19(1), 150–158.doi:10.1016/j.ecresq.2004.01.009
Geringer, J. (2003). Reflections on professional development: Toward high-quality teaching and learning. Phi Delta Kappan, 84(5), 373–380.doi:10.1177/003172170308400510
Gibbons, P. (2002). Scaffolding language, scaffolding learning: Teaching second language lear-ners in the mainstream classroom. Portsmouth, NH: Heinemann.
Greenfield, D. B., Jirout, J., Dominguez, X., Greenberg, A., Maier, M., & Fuccillo, J. (2009). Science in the preschool classroom: A programmatic research agenda to improve science readiness. Early Education and Development, 20(2), 238–264.doi:10.1080/10409280802595441
Halliday, M. A. K. (1993). Towards a language-based theory of learning. Linguistics and Education, 5(2), 93–116.doi:10.1016/0898-5898(93)90026-7
Han, S. S., & Weiss, B. (2005). Sustainability of teacher implementation of school-based mental health programs. Journal of Abnormal Child Psychology, 33(6), 665–679. doi:10.1007/s10802-005-7646-2
Hattie, J. (2009). The black box of tertiary assessment: An impending revolution. In L. H. Meyer, S. Davidson, H. Anderson, R. Fletcher, P. M. Johnston, & M. Rees (Eds.), Tertiary assessment & higher education student outcomes: Policy, practice & research (pp. 259–275). Wellington, New Zealand: Ako Aotearoa.
Henrichs, L. F., & Leseman, P. P. M. (2014). Early science instruction and academic language development can go hand in hand. The promising effects of a low-intensity teacher-focused intervention. International Journal of Science Education, 36(17), 2978–2995.
doi:10.1080/09500693.2014.948944
Hong, S.-Y., & Diamond, K. E. (2012). Two approaches to teaching young children science con-cepts, vocabulary, and scientific problem-solving skills. Early Childhood Research Quarterly, 27(2), 295–305.doi:10.1016/j.ecresq.2011.09.006
Huttenlocher, J., Vasilyeva, M., Cymerman, E., & Levine, S. (2002). Language input and child syntax. Cognitive Psychology, 45(3), 337–374.doi:10.1016/S0010-0285(02)00500-5
Justice, L. M., McGinty, A. S., Zucker, T., Cabell, S. Q., & Piasta, S. B. (2013). Bi-directional dynamics underlie the complexity of talk in teacher–child play-based conversations in class-rooms serving at-risk pupils. Early Childhood Research Quarterly, 28(3), 496–508.doi:10.1016/ j.ecresq.2013.02.005
Klahr, D. (2000). Exploring science: The cognition and development of discovery processes. Cambridge MA: MIT press.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174.doi:10.2307/2529310
Lee, Y., & Kinzie, M. B. (2012). Teacher question and student response with regard to cognition and language use. Instructional Science, 40(6), 857–874.doi:10.1007/s11251-011-9193-2
Lee, Y., Kinzie, M. B., & Whittaker, J. V. (2012). Impact of online support for teachers’ open-ended questioning in pre-k science activities. Teaching and Teacher Education, 28(4), 568– 577.doi:10.1016/j.tate.2012.01.002
Lutz, S. L., Guthrie, J. T., & Davis, M. H. (2006). Scaffolding for engagement in elementary school reading instruction. The Journal of Educational Research, 100(1), 3–20.doi:10.3200/JOER.100. 1.3-20
MacWhinney, B. (2000). The CHILDES Project: Tools for analyzing talk: Vol. 1. Transcription formats and programs (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.
Mashburn, A. J., Pianta, R. C., Hamre, B. K., Downer, J. T., Barbarin, O. A., Bryant, D.,… Howes, C. (2008). Measures of classroom quality in prekindergarten and children’s development of
academic, language, and social skills. Child Development, 79(3), 732–749. doi:10.1111/j.1467-8624.2008.01154.x
Massey, S. L., Pence, K. L., Justice, L. M., & Bowles, R. P. (2008). Educators’ use of cognitively chal-lenging questions in economically disadvantaged preschool classroom contexts. Early Education and Development, 19(2), 340–360.doi:10.1080/10409280801964119
Menninga, A., van Dijk, M., Steenbeek, H., & van Geert, P. (2017). Language use in real-time interactions during early elementary science lessons: The bidirectional dynamics of the language complexity of teachers and students. Language Learning, 67(2), 284–320.doi:10. 1111/lang.12219
Menninga, A., van Dijk, M., Wetzels, A., Steenbeek, H., & van Geert, P. (2017). Language use in kindergarten science lessons: Language production and academic language during a video feedback coaching intervention in kindergarten science lessons. Educational Research and Evaluation, 23(1–2), 26–51.doi:10.1080/13803611.2017.1292920
Mercer, N. (2000). Words and minds: How we use language to think together. London: Routledge. Nagy, W., & Townsend, D. (2012). Words as tools: Learning academic vocabulary as language
acquisition. Reading Research Quarterly, 47(1), 91–108.doi:10.1002/RRQ.011
Newton, D. P., & Newton, L. D. (2000). Do teachers support causal understanding through their discourse when teaching primary science? British Educational Research Journal, 26(5), 599– 613.doi:10.1080/713651580
Noell, G. H., Duhon, G. J., Gatti, S. L., & Connell, J. E. (2002). Consultation, follow-up, and implementation of behavior management interventions in general education. School Psychology Review, 31(2), 217–234.doi:10.1080/02796015.2002.12086152
Oliveira, A. W. (2009).“Kindergarten, can I have your eyes and ears?” Politeness and teacher directive choices in inquiry-based science classrooms. Cultural Studies of Science Education, 4(4), 803–846.doi:10.1007/s11422-009-9193-6
Oliveira, A. W. (2010). Improving teacher questioning in science inquiry discussions through professional development. Journal of Research in Science Teaching, 47(4), 422–453.doi:10. 1002/tea.20345
Powell, D. R., Diamond, K. E., Burchinal, M. R., & Koehler, M. J. (2010). Effects of an early literacy professional development intervention on head start teachers and children. Journal of Educational Psychology, 102(2), 299–312.doi:10.1037/a0017763
Reinke, W. M., Sprick, R., & Knight, J. (2009). Coaching classroom management. In J. Knight (Ed.), Coaching: Approaches & perspectives (pp. 91–112). Thousand Oaks, CA: Corwin Press. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic
motiv-ation, social development, and well-being. American Psychologist, 55(1), 68–78.doi:10.1037/ 0003-066x.55.1.68
Saavedra, A. R., & Opfer, V. D. (2012). Learning 21st-century skills requires 21st-century teach-ing. Phi Delta Kappan, 94(2), 8–13.doi:10.1177/003172171209400203
Scarcella, R. (2003). Academic English: A conceptual framework (The University of California Linguistic Minority Research Institute Technical Report No. 2003-1). Retrieved fromhttp:// escholarship.org/uc/item/6pd082d4
Schleppegrell, M. J. (2001). Linguistic features of the language of schooling. Linguistics and Education, 12(4), 431–459.doi:10.1016/S0898-5898(01)00073-0
Schleppegrell, M. J. (2004). The language of schooling: A functional linguistics perspective. Mahwah, NJ: Lawrence Erlbaum.
Schrooten, W., & Vermeer, A. (1994). Woorden in het basisonderwijs: 15.000 woorden aangebo-den aan leerlingen [Words in primary education: 15,000 words for stuaangebo-dents] . Tilburg: Tilburg University Press.
Seidel, T., Stürmer, K., Blomberg, G., Kobarg, M., & Schwindt, K. (2011). Teacher learning from analysis of videotaped classroom situations: Does it make a difference whether teachers
observe their own teaching or that of others? Teaching and Teacher Education, 27(2), 259– 267.doi:10.1016/j.tate.2010.08.009
Snow, C. E. (2014). Deep comprehension: What does it look like in two- to three-year-olds? [PowerPoint slides]. Retrieved from http://www.lucasacademie.nl/uimages/Catherine Snow_ECCESS201420282%29.pdf
Snow, C. E., & Uccelli, P. (2009). The challenge of academic language. In D. R. Olson & N. Torrance (Eds.), The Cambridge handbook of literacy (pp. 112–133). New York, NY: Cambridge University Press.
Spycher, P. (2009). Learning academic language through science in two linguistically diverse kindergarten classes. The Elementary School Journal, 109(4), 359–379.doi:10.1086/593938
Strathie, S., Strathie, C., & Kennedy, H. (2011). Video enhanced reflective practice. In H. Kennedy, M. Landor, & L. Todd (Eds.), Video interaction guidance: A relationship-based inter-vention to promote attunement, empathy and wellbeing (pp. 170–180). London: Jessica Kingsley.
Sullivan, G. M., & Feinn, R. (2012). Using effect size – Or why the P value is not enough. Journal of Graduate Medical Education, 4(3), 279–282.doi:10.4300/JGME-D-12-00156.1
Thurston, A., Christie, D., Howe, C. J., Tolmie, A., & Topping, K. J. (2008). Effects of continuing professional development on group work practices in Scottish primary schools. Journal of In-Service Education, 34(3), 263–282.doi:10.1080/13674580802264803
Todman, J. B., & Dugard, P. (2001). Single-case and small-n experimental designs: A practical guide to randomization tests. Mahwah, NJ: Lawrence Erlbaum Associates.
van den Heijkant, C., Quak, G., van Swet, J., Vloet, K., de Vos, M., & van der Wegen, R. (2006). School video interactie begeleiding: Van meerdere kanten bekeken [School video interaction guidance: Viewed from multiple angles]. Antwerpen/Apeldoorn: Garant.
van Hout, R. W. N. M., & Vermeer, A. (2007). Comparing measures of lexical richness. In H. Daller, J. Milton, & J. Treffers-Dahl (Eds.), Modelling and assessing vocabulary knowledge (pp. 93– 115). Cambridge, UK: Cambridge University Press.
van Keulen, H., & Sol, Y. (2012). Talent ontwikkelen met wetenschap en techniek [Developing talent with science and technology]. Utrecht: Centrum voor Onderwijs en Leren Universiteit Utrecht.
Wasik, B. A., & Bond, M. A. (2001). Beyond the pages of a book: Interactive book reading and language development in preschool classrooms. Journal of Educational Psychology, 93(2), 243–250.doi:10.1037/0022-0663.93.2.243
Wasik, B. A., Bond, M. A., & Hindman, A. (2006). The effects of a language and literacy interven-tion on Head Start children and teachers. Journal of Educainterven-tional Psychology, 98(1), 63–74.
doi:10.1037/0022-0663.98.1.63
Wellington, J., & Osborne, J. (2001). Language and literacy in science education. London: McGraw-Hill Education.
Wetzels, A. F. M. (2015). Curious minds in the classroom: The influence of video feedback coaching for teachers in science and technology lessons (Doctoral dissertation, University of Groningen). Retrieved from https://www.rug.nl/research/portal/publications/curious-minds-in-the-classroom%285c6a3c87-2f8d-4f50-af9c-2b23e5d8d6b3%29.html
Wolf, S. A., Mieras, E. L., & Carey, A. A. (1996). What’s after “What’s that?”: Preservice teachers learning to ask literary questions. Journal of Literacy Research, 28(4), 459–497. doi:10. 1080/10862969609547937
Wolff, C. E., van den Bogert, N., Jarodzka, H., & Boshuizen, H. P. A. (2015). Keeping an eye on learning: Differences between expert and novice teachers’ representations of classroom management events. Journal of Teacher Education, 66(1), 68–85. doi:10.1177/ 0022487114549810
Wong Fillmore, L., & Snow, C. E. (2002). What teachers need to know about language. In C. Temple Adger, C. E. Snow, & D. Christian (Eds.), What teachers need to know about language (pp. 7–47). McHenry, IL: Delta Systems Company and the Center of Applied Linguistics. Yifat, R., & Zadunaisky-Ehrlich, S. (2008). Teachers’ talk in preschools during circle time: The
case of revoicing. Journal of Research in Childhood Education, 23(2), 211–226.doi:10.1080/ 02568540809594656
Zwiers, J. (2008). Building academic language: Essentials for content classrooms, grades 5-12. Newark, DE: International Reading Association.
