As teacher learning is a complex phenomenon, mentoring preservice and beginning teachers is one of the most efficient approaches. Yet the challenge for many mentor teachers is incorporating effective mentoring practices to support mentees’ learning to teach process. There are some models on mentoring. Some models focus on interaction between mentor and mentees, such as MEntor (teacher) Roles In Dialogues (MERID) Model (Hennissen et al., 2008). Using mentor-mentee dialogues in two aspects, directiveness and input, the MERID model provides four types of mentor-mentee interactions. With the MERID model, it’s possible to investigate mentor-mentee interaction. However, its limitation resides in not discussing area-specialized teaching. There is also a mentoring model describing the process for clinical professionals; the Clinical Supervision Model focuses on constant observation (Baltaci et al., 2013; Cogan, 1973). In Türkiye, teachers must complete a three-day professional development course to be certified as mentor teachers. This professional development is based on the Clinical Supervision Model.  However, the aim of a mentor teacher with area specialization should be guiding “learning to teach” process of preservice or beginning teachers. Specifically, for mathematics and science teaching, the Five-Factor Mentoring Model by Hudson and Skamp (2003) provides mentoring practices under five factors:  personal attributes, system requirements, pedagogical knowledge, modelling and feedback. Even though this model provides a broad understanding of mentoring for area-specialized teaching, there is a need for comprehensive and empirical area-specialized mentoring practices (Aslan-Tutak et al., 2024). For guiding the complex “learning to teach” process of preservice and beginning teachers, there is a need for an elaborated approach, a model to capture the rich nature of area-specialized teaching.

To develop a model, Mentoring for Area Specialized Teaching (MAST), for effective mentoring, the authors (mathematics and science teacher educators) formed a multi-city, large research group in Türkiye. This project is funded by TUBITAK - Scientific and Technological Research Council of Türkiye for 2021-2024. In this presentation, the authors will focus on the third dimension of the MAST model, Area of Specialized Teaching. This dimension provides a detailed description of “what” and “how” for area-specialized effective mentoring.

The unique contribution of this research stems from taking all three stakeholders (mentors, preservice teachers and teacher educators) into consideration to capture the richness of the mentoring process. A mixed method research is conducted to address the research question, “what constitutes effective area-specialized mentoring practices for teaching practicums in the mathematics, physics, chemistry, and biology disciplines?”.  A descriptive study was conducted with secondary school mentors in mathematics, physics, chemistry, and biology (MPCB). Then, a qualitative study was conducted with a smaller group to understand MPCB mentor teachers’ understanding of effective mentoring. Meanwhile, mathematics and science teacher educators provided expert opinions for the Delphi study on effective mentoring practices. Preservice MPCB teachers attending teaching practicum in those three cities participated in a descriptive study to capture their experiences and their needs regarding effective mentoring in area-specialized teaching. The MAST model, how mentor teachers, teacher educators and preservice teachers perceive “effective mentoring practices” may provide fruitful discussion for the NW 01 audience, specifically regarding the call of Ecologies of Teacher Induction and Mentoring in Europe (TIME).

The first step was to start Delphi study with teacher educators. The purpose of the Delphi method is to reach a consensus among experts through a cyclic process of data collection and analysis (Hsu & Sandford, 2007). For this study, teacher educators from Türkiye and other countries were contacted. Fifteen mathematics teacher educators and twenty-five science (physics, chemistry, biology) teacher educators voluntarily participated in the first round of data collection. At first, a questionnaire consisting of open-ended questions about effective mentoring practices for MPCB teaching was sent. Three themes emerged from the inductive analysis of the participants’ answers. The research team formed one item for each of the codes under these themes. A survey consists of 108 items, with 10 Likert type items constructed and sent for the second round of data collection. At the time of the second round of the Delphi study, the data collection from mentor teachers started with a descriptive study. MPCB teachers from Istanbul, Ankara and Trabzon who hold mentoring certification were reached and 104 mentor teachers participated. Later, a small group of these participants, 29 mentor teachers (11 mathematics, 18 science), were selected based on descriptive results to conduct the qualitative phase of the investigation. The transcribed individual interviews were then analyzed with open coding. To reach a model representing both teacher educators' and mentors' perspectives, the open codes were analyzed deductively with the themes and codes from the Delphi study. However, a vast number of open codes didn’t match with any codes from the Delphi study. Thus, an inductive approach of qualitative analysis is used for mentor teacher interviews. Data analysis yielded three dimensions of effective mentoring for area-specialized mentoring: I. Triad Interactions, II. General Pedagogy, III. Area Specialized Teaching. Then, the items that teacher educators had a consensus on in the second round match with these three dimensions and their associated themes and codes. Later, preservice teachers from the three cities were asked about their experiences regarding effective mentoring during teacher practicum and their mentee needs. The preservice teachers’ (n=344) answers to open-ended questions were analyzed deductively through three dimensions with associated themes and codes.

Three dimensions of the MAST model describe effective mentoring practices to support preservice teachers' process of learning to teach. In this presentation, the researchers will discuss the third dimension. The first theme, III.1.Content Knowledge, describes the mentor teacher's practices to improve pre-service teachers' content knowledge. As content knowledge is necessary for PCK, effective mentoring needs to take into consideration supporting mentees in this regard. III.2. Mentoring Practices about Teaching describes “what” mentors should do with mentees. There are six codes under this theme: a.Teaching in the Authentic Context, b.Designing and Implementing Instructional Tasks, c.Evaluation, Reflection, Revision. Having two more codes differentiating science and mathematics mentoring was interesting yet unsurprising. The science teaching codes are d.Laboratory Teaching, e.Inquiry-based Learning, while the mathematics teaching codes are d.Conceptual Learning, e.Teaching for Mathematical Skills. The unique aspect of the MAST model is the third theme, III.3. Mentor-Mentee Interactions, which describes “how” mentors should guide mentees while conducting practices discussed in theme III.2. This theme is not about the general interaction between mentor-mentee but how mentor is implementing area-specialized guidance. The codes for this theme are a.Modeling, b.Mentor Sharing, c.Designing and Implementing Together, d.Reflection, e.Feedback, f.Balancing Intervention and Guidance. The last three themes of this dimension are III.4.Curriculum Knowledge, III.5.Instructional Materials and Technology, III.6.Student Ideas. These themes, indeed, are components of PCK. The majority of the mentors did not use the term PCK, which may indicate their limited knowledge of the terminology. However, they were aware of what a preservice teacher needs to know to teach the subject effectively. Thus, based on data from all three stakeholders, when the themes and dimensions are considered, they imply how PCK needs to be addressed in mentoring. Acknowledgement This research is funded by the Scientific and Technological Research Council of Turkey (TUBITAK) Project No:220K086.

Aslan-Tutak, F., Şahin, A., Civan, B. S., & Ertas, G. (2024). Adaptation and validation of Mentoring for Effective Mathematics Teaching (MEMT) – Mentee and Mentor scales to Turkish. Mentoring & Tutoring: Partnership in Learning, 32(4), 466–488. Baltacı, G. S., Bulunuz, N., Bulunuz, M., Gürsoy, E., Kesner, J., & Salihoğlu, U. (2013). Clinical Supervision Model to Improve Supervisory Skills of Cooperating Teachers and University Supervisors during Teaching Practice. Hacettepe University Journal of Education, Special Issue (1), 191-203. Cogan, M. L. (1973). Clinical Supervision. Houghton Mifflin. Hennissen, P., Crasborn, F., Brouwer, N., Korthagen, F., & Bergen, T. (2008). Mapping Mentor Teachers' Roles in Mentoring Dialogues. Educational Research Review, 3(2), 168-186. Hudson, P., Skamp, K. R. (2003). Mentoring preservice teachers of primary science, The Electronic Journal of Science Education, 7(1).