Learning student-centered collaboration in the social network teacher education

Beschreibung

Collaboration among teachers is significant for the immediate handling of professional tasks, such as team teaching or in implementing legal requirements such as inclusive schooling, and it has a positive impact on students’ learning outcomes. Collaboration is usually considered a professional standard for educators and is demanded as content in teacher education. However, novice teachers often feel inadequately prepared for collaborative activities. There is a lack of empirical knowledge regarding what current student teachers are learning about collaborative activities, particularly those focused on students’ learning processes (student-centered collaboration) in their education.

In this project, we are interested in the learning opportunities for student teachers from a socio-constructive and a social network perspective. First, this investigation aims to understand what student teachers learn in social interactions and with whom, in terms of student-centered collaboration, referencing which tools are used, and how significant they perceive this for their professional development. Second, we seek to examine the extent to which the perspectives of student teachers in primary education (PE) and secondary education level 1 (S1) diverge. Third, data will be collected using a mixed-methods approach, wherein student teachers are tasked with describing their social network maps either to a peer or to a chatbot. The study will investigate how conversations between student teachers differ from those with a chatbot in terms of content clarity.

Approximately 200 student teachers from PE and S1 will be surveyed about learning opportunities related to student-centered collaboration at the end of their studies. A data collection tool will be developed, whereby student teachers use a web application to enter aspects of the above research question on an egocentric network map. Subsequently, an initial group of student teachers will discuss their egocentric network map with fellow student teachers. In previous applications, these data have provided a rich dataset, but a trained person capable of asking probing questions has been lacking. A second group will conduct this conversation with a chatbot based on a large language model (a type of AI algorithm). The goal is to develop the chatbot to assume the role of a researcher and ask stimulating follow-up questions.

The data will be analyzed using thematic qualitative text analysis using MAXQDA software. Collaborative learning opportunities will be described, codes quantified, and differences between PE and S1 student teachers quantitatively calculated. It is hypothesized that S1 student teachers, due to a longer study duration compared to PE student teachers, will reference more people and provide a more in-depth description of collaboration. In addition, a method will be explored to aggregate egocentric network maps into an overall network map, aiming to identify extant social structures in learning opportunities and compare those in PE and S1. To assess the data quality of the two aforementioned groups (fellow student teachers vs. chatbot), responses regarding content clarity will be rated using evaluative qualitative content analysis, with differences calculated quantitatively.

Student-centered collaboration plays a crucial role in enhancing students' learning. The aim is to understand which social learning opportunities are available to student teachers and how significant they are for their professional development. The comparison between PE and S1 could provide indications of the structural conditions of such learning opportunities. The data collection tool with the chatbot has the potential to collect quantitative data from many student teachers as well as qualitative descriptions of individual cases. Notably, the chatbot could serve as a reflective tool within teacher education programs.