For several decades now, assessment has become an increasingly pressing educational priority. Teacher and school accountability systems have come to be based on analysis of large-scale, standardized summative assessments. As a consequence, assessment now dominates most conversations about reform, particularly as a measure of teacher and school accountability for learner performance. Behind the often heated and at times ideologically gridlocked debate is a genuine challenge to address gaps in achievement between different demographically identifiable groups of students. There is an urgent need to lift whole communities and cohorts of students out of cycles of underachievement. For better or for worse, testing and public reporting of achievement is seen to be one of the few tools capable of clearly informing public policy makers and communities alike about how their resources are being used to expand the life opportunities for their children. This course is an overview of current debates about testing, and analyses the strengths and weaknesses of a variety of approaches to assessment. The course also focuses on the use of assessment technologies in learning. It will explore recent advances in computer adaptive and diagnostic testing, the use of natural language processing technologies in assessments, and embedded formative assessments in digital and online curricula. Other topics include the use of data mining and learning analytics systems in learning management systems and educational technology platforms. Participants will be required to consider issues of data access, privacy and the challenges raised by ‘big data’ including data persistency and student profiling.
제공자:
학습을 위한 평가
일리노이대학교 어버너-섐페인캠퍼스이 강좌에 대하여
제공자:
일리노이대학교 어버너-섐페인캠퍼스
The University of Illinois at Urbana-Champaign is a world leader in research, teaching and public engagement, distinguished by the breadth of its programs, broad academic excellence, and internationally renowned faculty and alumni. Illinois serves the world by creating knowledge, preparing students for lives of impact, and finding solutions to critical societal needs.
강의 계획표 - 이 강좌에서 배울 내용
Course Orientation + Intelligence Tests
This course is an overview of current debates about testing, and analyses of the strengths and weaknesses of a variety of approaches to assessment. The module also focuses on the use of assessment technologies in learning. It will explore recent advances in computer adaptive and diagnostic testing, the use of natural language processing technologies in assessments, and embedded formative assessments in digital and online curricula. Other topics include the use of data mining and learning analytics in learning management systems and educational technology platforms. The module also considers issues of data access, privacy, and the challenges raised by ‘big data’ including data persistency and student profiling. A final section addresses the processes of educational evaluation. Video presenters include Mary Kalantzis, Bill Cope, Luc Paquette, and Jennifer Greene.
Kinds of Assessments
The word "standard" is used in two quite different ways in testing theory and practice: to create a common measure of learning in "standardized assessments"; and the generalized and measurable objectives of learning. Sometimes standardized assessments are used to determine the outcomes of standards-based education, but often not. Standards-based assessment can also be criterion-referenced, and self-referenced.
New Assessments in the Digital Age
Computer-mediated assessments can be used to mechanize, and so make more efficient, traditional select-and-supply response assessments. However, new opportunities also present themselves in the form of technologies and assessment processes called "learning analytics."
Educational Data Mining + Evaluation
In this module, Luc Paquette discusses educational data mining – a new generation of techniques with which to analyze student learning for the purposes of assessment, evaluation, and research. Finally, Jennifer Greene explores theories and practices of evaluation. Assessment data may be used to support evaluations, however evaluation is a considerably broader process.
검토
학습을 위한 평가의 최상위 리뷰
Supports students in rethinking norm-referenced standards for assessment, in comparison with mastery learning. Also introduces educational data mining and whole-program evaluation.
This was my first course in the field of education and I really enjoyed it despite the fact I got scared in the start with this new field..
Very helpful to understand Assessment throughout the education sector. Got an idea to adopt newer assessment methods .
Extremely relevant course, though I wish I had enough prior knowledge to understand Data Mining part.
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