Learning in Immersive and Modelling Environments

This research went beyond documenting changes in participants’ science knowledge and confidence in ability in learning and teaching science; it aimed to understand in finer detail how these changes came about. In particular, understanding how participants’ experiences in the immersive and modelling environments and their perceptions of these environments contributed to the changes in their understanding and confidence in science. Also, the study aimed to explore how changes in one component may trigger or support a change in another, as this might inform the design of learning experiences to make them even more productive and for more preservice teachers.

This was investigated by exploring participants’ levels of engagement during the study, which evolved from the ability of the immersive and modelling environments to engage participants in the learning process and the effects of this on their knowledge, understanding and, therefore, confidence. As discussed earlier, all participants’ actions and interactions during their engagement with the Omosa and Omosa NetLogo resources, as well as during the concept map construction process, were recorded using Camtasia and/or audio recording. The aim of these recordings was to investigate how and why the study interventions may have contributed to changes in the participants’ understanding and confidence. The recordings were transcribed in full and then engagement coding categories and subcategories were developed based on the coding scheme of Ainsworth and Th Loizou (2003), with some additions and modifications to accommodate the nature

of this study (Table 3.4). Extra coding categories—flow of engagement (verbal and non- verbal), technical engagement (positive and negative) and collaborative engagement— and a subcategory checking understanding were added. Figure 3.7 shows all engagement categories and their subcategories developed for the study. Examples of these categories and subcategories and their definition are shown in the coding scheme provided in Table 3.4.

The qualitative analysis software NVivo was used for coding and analysing of the data included in the transcripts. Nodes were created in NVivo for all developed engagement categories and subcategories; each segment of the transcripts was dragged to the appropriate node (category).

Table 3.4: The engagement categories and subcategories developed for this study and their definition (coding scheme)

Category Ainsworth’s definition Modified definition

Goal-based explanation

Self-explanations classified as goal driven if student imposed a goal or purpose for an action

Cognitive engagement

Scored if participant made decision, structured, questioned, decided what to do next and how

Principle- based explanation Category scored if participants made reference to underlying domain principles in an elaborated way

Scored if participants explained idea or concept with elaboration (relating the idea or concept to the condition or situation), consolidated, built new concept, words or knowledge

Paraphrasing Includes elaboration of

the current sentence/diagram

Scored if participant made meaning from relationships, related information, used terms provided in the learning environment using the same words from the information they had

Noticing coherence

Indicates when students related what they were presently studying to a previous item

Scored if participants related what they were presently studying to a previous item

Monitoring

Positive Statements indicating

that student understands the material

Scored if participant indicated that they understood the material

Negative Statements indicating

that student did not understand the material

Scored if participant indicated that they did not understand the material

Checking understanding

Scored if participant made sure they understood materials they were exposed to

Flow of engagement

Verbal (Dowling & Ahern,

2018)

Verbal expression that expressed participants’ engagement

Non-verbal (Argyle, 1988; Dowling

& Ahern, 2018)

Physical behaviours including body movement, facial expressions, differences in spoken tone volume or gestures that expressed, demonstrated or implied participants’ enjoyment and

engagement with the learning environment

Technical engagement:

Positive Any positive technical issues participants faced

while learning using the technology learning environment related to navigation and use of the technology environment

Negative Any negative technical issues participants faced

while learning using the technology learning environment related to navigation and use of the technology environment

Collaboration engagement

Collaboration Scored if both participants were actively

involved and working together towards the goal (answering the questions and writing the final report in the guidebook)

The frequency of all engagement categories and subcategories and the time spent in each cognitive engagement subcategory was calculated for each session and for different stages during the sessions. The results of these calculations were examined in relation to participants’ understanding and confidence. Cognitive engagement of learners is of particular importance and concern because of its strong relationship with learning (Casimiro, 2016). Evidence of a high frequency of cognitive engagement indicates that participants spent a majority of their time cognitively involved in the learning resources, which was expected to have a positive effect on their knowledge and understanding. The calculation results from the other engagement categories and their subcategories were also investigated in relation to the participants’ understanding and confidence.

To examine the effect of participants’ perceptions about their learning in the immersive and modelling environments in terms of changes in their understanding and confidence, the perception data collected in the pre-test/post-test interviews and in the short interviews (Table 3.2) were transcribed and compiled. Possible associations between these perceptions and participants’ knowledge and understanding and confidence were explored.

To ensure that the data analysis was reliability, two people coded the data. Moreover, to ensure that there was inter-rater reliability, cross-coding was performed by a faculty member recoding one of the transcripts using the developed coding scheme matrix. The agreement was ~60%, which was considered low; it seemed there was an unclear grasp of the difference between two of the coding subcategories by the second coder. This issue was discussed with the supervisory team and, as a consequence a second cross-coding was performed by a PhD student with more clarification of the categories and

subcategories, after which the percentage agreement was ~ 76%. This was deemed an appropriate level of agreement by the supervisory team.

3.9 Summary

In summary, a small-N design study was conducted with a group of first year preservice primary teachers with low science CK and low confidence in their ability in science. The study consisted of engaging the participants to learn ecology concepts using inquiry activities in an immersive environment and a modelling environment. The study investigated the effect on their science CK and confidence in their ability in science of engaging preservice teachers with such environments. The study was conducted over two sessions; the immersive environment in the first session and the modelling environment in the second session. Two guidebooks were developed following the 5Es learning cycle model to help participants organise their learning and make meaning of their learning experiences. Data were collected from a variety of sources including survey, interviews, participants’ concept maps, participants’ responses in their guidebooks, and Camtasia and audio recordings of participants’ actions and interactions, to address the study research questions. Multiple data analysis methods, including quantitative and qualitative methods, were used to analyse the data and answer the research questions.

The following two chapters report the results of the study based on the methodologies applied to collect and analyse the data. The first results chapter provides examples of the data analysis processes for one dyad of preservice teachers and discusses the main findings in light of the research questions that the study sought to answer. The second results chapter reports the findings for all the dyads participating in this study.