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321.
322.
Wolfgang Schnotz Justus Böckheler Harriet Grzondziel 《European Journal of Psychology of Education - EJPE》1999,14(2):245-265
Computer-based multimedia learning environments make it possible to present interactive animated pictures, which can be manipulated for active exploratory learning and which allow the dynamic behavior of complex subject matter to be displayed. Due to the large range of possibilities for exploratory interaction, such learning environments seem well suited for co-operative learning in which different learners analyse the presented subject matter from different perspectives. This paper first describes a theoretical framework for learning from texts and pictures together with an analysis of possible effects of animation and interactivity on knowledge acquisition. It then presents two empirical studies in which knowledge acquisition from interactive animated pictures was compared with knowledge acquisition from static pictures under the conditions of individual learning (Study I) and of cooperative learning (Study II). In Study I, learning with interactive animated pictures resulted in a superior encoding of detail information, but did not facilitate performance of mental simulation tasks. In Study II, learning with interactive animated pictures resulted both in inferior encoding of detail information and poorer results in mental simulations. These findings and the analysis of discourse protocols of subjects’ co-operation suggest that exploratory learning with interactive animated pictures is associated with extraneous cognitive load and that this load can be further increased by the co-ordination demands of co-operative learning. Although animated pictures may provide external support for mental simulations, they do not appear to be generally beneficial for learning, because they can prevent individuals from performing relevant cognitive processes. 相似文献
323.
Judith Stanja Wolfgang Gritz Johannes Krugel Anett Hoppe Sarah Dannemann 《British journal of educational technology : journal of the Council for Educational Technology》2023,54(1):58-75
Formative assessment is considered to be helpful in students' learning support and teaching design. Following Aufschnaiter's and Alonzo's framework, formative assessment practices of teachers can be subdivided into three practices: eliciting evidence, interpreting evidence and responding. Since students' conceptions are judged to be important for meaningful learning across disciplines, teachers are required to assess their students' conceptions. The focus of this article lies on the discussion of learning analytics for supporting the assessment of students' conceptions in class. The existing and potential contributions of learning analytics are discussed related to the named formative assessment framework in order to enhance the teachers' options to consider individual students' conceptions. We refer to findings from biology and computer science education on existing assessment tools and identify limitations and potentials with respect to the assessment of students' conceptions.
Practitioner notes
What is already known about this topic- Students' conceptions are considered to be important for learning processes, but interpreting evidence for learning with respect to students' conceptions is challenging for teachers.
- Assessment tools have been developed in different educational domains for teaching practice.
- Techniques from artificial intelligence and machine learning have been applied for automated assessment of specific aspects of learning.
- Findings on existing assessment tools from two educational domains are summarised and limitations with respect to assessment of students' conceptions are identified.
- Relevent data that needs to be analysed for insights into students' conceptions is identified from an educational perspective.
- Potential contributions of learning analytics to support the challenging task to elicit students' conceptions are discussed.
- Learning analytics can enhance the eliciting of students' conceptions.
- Based on the analysis of existing works, further exploration and developments of analysis techniques for unstructured text and multimodal data are desirable to support the eliciting of students' conceptions.