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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 notesWhat 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.What does the paper add 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.Implications for practice and/or policy 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. [ABSTRACT FROM AUTHOR]

One of the most challenging tasks for faculty in their role as a teacher is to design, administrate, and evaluate formative assessments. This results in a lot of faculty not utilizing the formative assessments as a form of student feedback and make regular changes to the course. In this study, we explore the role of technology in helping faculty implement and analyze formative assessments. We believe the advancement of technology and its increasing applications in education may result in development of novel solutions to this impediment through learning analytics. This paper explores the use of such data analytics to address the challenges impeding the capacity of instructors to provide personalized feedback at scale. The study was implementing in the Computer Science and Engineering Department where a group of faculties who teach undergraduate engineering students. The faculty designed and administered a set of formative assessments in multiple courses through the learning management system. The data collected from the formative assessments was analyzed using learning analytics and the results were used to provide actionable recommendations that could better support students to achieve the learning outcomes of the course. The data was analyzed to understand and optimize the learning process of the students in the course. The results of the paper will provide insights to engineering educators on how they could take a data driven approach and harness the power of technology to better support their students. This approach will also help direct students towards deeper and self-regulated learning. [ABSTRACT FROM AUTHOR]

The Massachusetts Institute of Technology (MIT) OpenCourseWare (OCW) was launched in 2001. It is one of the earliest Open Educational Resources (OER). MIT OCW has published more than 2,400 courses which are available at no cost, the majority of which are STEM related. The purpose of this exploratory study was to examine the pedagogical strategies through reviewing instructor insights of 15 MIT OCW STEM courses using thematic analysis. The most effective pedagogical strategies used found by instructors were active learning, personalizing instruction, engaging learners, providing feedback, building learning community, and clarifying learning objective. Instructors used in-class formative assessment, such as quizzes and oral exams, for just-in-time teaching and online automatic assessment environments for students' selfassessment. The primary summative assessments were final exams and projects. Instructors encountered challenges such as assessing students' learning and changing pedagogical beliefs. Implications for practice were discussed as well. [ABSTRACT FROM AUTHOR]