Details

Autor(en) / Beteiligte

• Nickl, Michael
• Huber, Sina A.
• Sommerhoff, Daniel
• Codreanu, Elias
• Ufer, Stefan
• Seidel, Tina

Titel

Video-based simulations in teacher education: the role of learner characteristics as capacities for positive learning experiences and high performance

Ist Teil von

• International Journal of Educational Technology in Higher Education, 2022-12, Vol.19 (1), p.45-45, Article 45

Ort / Verlag

Cham: Springer International Publishing

Erscheinungsjahr

2022

Quelle

ERIC

Beschreibungen/Notizen

• Assessing students on-the-fly is an important but challenging task for teachers. In initial teacher education, a call has been made to better prepare pre-service teachers for this complex task. Advances in technology allow this training to be done through authentic learning environments, such as video-based simulations. To understand the learning process in such simulations, it is necessary to determine how cognitive and motivational learner characteristics influence situative learning experiences, such as the perception of authenticity, cognitive load, and situational motivation, during the simulation and how they affect aspects of performance. In the present study, N  = 150 pre-service teachers from German universities voluntarily participated in a validated online video-based simulation targeting on-the-fly student assessments. We identified three profiles of learner characteristics: one with above average knowledge, one with above average motivational-affective traits, and one with below average knowledge and motivational-affective traits. These profiles do not differ in the perception of the authenticity of the simulation. Furthermore, the results indicate that the profiled learners navigate differently through the simulation. The knowledgeable learners tended to outperform learners of the other two profiles by using more learning time for the assessment process, also resulting in higher judgment accuracy. The study highlights how learner characteristics and processes interact, which helps to better understand individual learning processes in simulations. Thus, the findings may be used as a basis for future simulation research with a focus on adaptive and individual support.