Developing Digital (and) Teaching Skills

Research indicates that lecturers and teachers should engage in regular introspection regarding their teaching practices and competences as a means to enhance their pedagogical aptitude (e.g., Lombarts et al., 2009). Within tertiary education establishments, such as universities, the evaluation of lectures has evolved into a customary instrument for gauging the caliber of academic instruction. Theoretically, educators can harness student evaluations of teaching for self-evaluation. Nevertheless, these all-encompassing tools (1) are predominantly structured for conclusive and evaluative intentions, (2) frequently neglect criteria essential for effective and proficient teaching, (3) and do not furnish precise interventions tailored to discernible gaps. To counterbalance these deficiencies, the Education Development Unit at the University of Bern has devised specialized digital self-assessment instruments, which furnish targeted assistance to educators in refining their teaching methodologies and competencies. At our poster, we will introduce two such tools: (1) SELEVOR, designed to aid educators in conceiving lectures, and (2) SEIDL, which evaluates the levels of digital teaching competence among educators.
SELEVOR assesses teaching concepts based on seven synthesized principles drawn from contemporary theories and models in the psychology of learning and teaching, including works by, among other, Hattie (2012): (1) constructive alignment, (2) target group orientation, (3) problem focus, (4) choice of content, (5) elaboration, (6) adaptive teaching, and (7) teacher engagement. Each principle is evaluated using a four-point Likert scale consisting of five items. Users promptly receive feedback on the alignment of their lecture concepts with each of the seven principles, along with specific algorithmically-derived interventions. Notably, SELEVOR has undergone validation, culminating in the presentation of its refined version. With assistance from SEIDL, educators can seamlessly record and contemplate their personal proficiencies in digitalized instruction through an easily accessible and tailored approach. In contradistinction to SELEVOR, SEIDL employs case vignettes for executing level classification (an adaptation of DigCompEdu, Redecker 2017) within the competency domains of planning, implementation, evaluation, and interaction (a modification of IN.K19, Sailer et al., 2021).
Notice: Both paragraphs were proof-read with the use of ChatGPT (OpenAI, 2023)

References:
Hattie, J. (2012). Visible learning for teachers: Maximizing impact on learning. New York: Routledge.
Lombarts, K. M., Bucx, M. J., & Arah, O. A. (2009). Development of a system for the evaluation of the teaching qualities of anesthesiology faculty. The Journal of the American Society of Anesthesiologists, 111(4), 709-716.
OpenAI. (2023). ChatGPT (Aug 3 version) [Large language model]. https://chat.openai.com/chat
Redecker, C. (2017). European Framework for the Digital Competence of Educators: DigCompEdu. In Y. Punie (Ed.), EUR 28775 EN (p. 93). Publications Office of the European Union. https://doi.org/10.2760/178382
Sailer, M., Stadler, M., Schultz-Pernice, F., Franke, U., Schöffmann, C., Paniotova, V., Husagic, L., & Fischer, F. (2021). Technology-related teaching skills and attitudes: Validation of a scenario-based self-assessment instrument for teachers. Computers in Human Behavior, 115, 106625. https://doi.org/10.1016/J.CHB.2020.106625