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This research study explores the potential of a pedagogical model of self-regulated learning supported with Artificial Intelligence (AI) chatbots to enhance self-expression and reflective writing skills for novice EFL student teachers at Faculty of Education, Assiut University. The study adopted a pre-post quasi-experimental design, that starts with the identification of the necessary self-expression and reflective writing skills for the target participants (50 fresh EFL student teachers at Assiut University who were purposively selected using a screening questionnaire based on their basic IT literacy skills). A pre-test was administered to assess their initial skill levels in self-expression and reflective writing. Then, an intervention was implemented in the form of a pedagogical model designed around the principles of self-regulated learning and situated language learning, which guided the use of AI chatbots (Bing, ChatGPT, and Google Bard). This model was initially piloted on a small sample (n = 10) of EFL student teachers to check validity and reliability and then experimented with the research participants for 8 weeks during the first semester of the academic year 2023/24. Following the intervention, a post-test was conducted to measure the participants' levels of self-expression and reflective writing skills after being exposed to the interventional model, aiming to identify any improvements gained from the intervention. The results indicated a positive effect with noticeable enhancements in the EFL student teachers' skills. This suggests the potential effectiveness of the model in fostering self-expression and reflective writing skills and developing EFL student teachers' general language proficiency and IT literacy. [This paper was published in "Academic Journal of Faculty of Education, Assiut University" v40 n9 p1-50 2024.]

Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that focuses on low-inference instructional practices, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two distinct educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. This is also the first study that applies NLP to measure a teaching practice that has been demonstrated to be particularly effective for students with special needs. We confront two challenges inherent in NLP-based instructional analysis, including noisy and long input data and highly skewed distributions of human ratings. Our results suggest that pretrained Language Models (PLMs) demonstrate performances comparable to the agreement level of human raters for variables that are more discrete and require lower inference, but their efficacy diminishes with more complex teaching practices. Interestingly, using only teachers' utterances as input yields strong results for student-centered variables, alleviating common concerns over the difficulty of collecting and transcribing high-quality student speech data in in-person teaching settings. Our findings highlight both the potential and the limitations of current NLP techniques in the education domain, opening avenues for further exploration.

Community banks are critical for local economies, yet research on their corporate governance has been scarce due to limited data availability. We explore a unique, proprietary dataset of board membership and meeting minutes of failed community banks to present several stylized facts regarding their board structure and meetings. Community bank boards have fewer members and a higher percentage of insiders than larger publicly traded banks, and experience little turnover during normal times. Their meetings are held monthly and span about two hours. During times of distress, community bank boards convene less often in regularly scheduled meetings in lieu of impromptu meetings, experience higher turnover, particularly among their independent directors, and their meeting tone switches from neutral to significantly negative. Board attention during distressed times shifts towards discussion of capital and examination oversight, and away from lending activities and meeting formalities. [ABSTRACT FROM AUTHOR]