NB Language of instruction is English
Description
In this course, students will:
1. Implement data-based problem-solving / data-based instruction
2. Critically evaluate the research surrounding data-based instruction.
Data-based problem-solving (also known as data-based instruction) refers to the use of data to build effective educational programs for students with learning and behavioral difficulties.
The course begins with a description of a problem-solving approach to learning and behavior. A system-wide problem-solving model is described: Response to Intervention (RTI)/ Multi-tiered Systems of Support (MTSS).
The emphasis then turns to data-based problem-solving at the individual level, and the use of Curriculum-Based Measurement (CBM). CBM (in Dutch, Continue Voortgangsmonitoring, CVM) is a progress-monitoring system specifically designed for implementation of data-based problem-solving for individuals with learning difficulties.
Students will learn the skills and techniques needed to implement CBM within a problem-solving approach for an individual. Students also will critically evaluate the research on problem-solving approaches and CBM.
Course objectives
Students will:
1. Describe what a problem-solving approach is, the factors that led to the development of a problem-solving approach, and the steps to problem solving.
2. Describe a specific problem-solving model, Response to Intervention/Multi-tiered Systems of Support, and discuss the potential advantages and disadvantages of a tiered systems of instruction.
3. Critically evaluate and discuss the research on data-based problem-solving / data-based instruction and CBM progress monitoring.
4. Implement data-based problem-solving using Curriculum-based Measurement (CBM), a system for closely monitoring the progress of, and evaluating the effects of, instructional programs for individuals with learning difficulties.
5. Prepare a data-based problem-solving report and present it to others in the class.
Mode of instruction
Lectures, presentations, and discussion.
Assessment method
Critical analysis of research articles, presentations, and progress-monitoring project. Guidelines about assignment(s), including AI use, can be found on Brightspace. Grades are based on the critical analyses of the research articles and presentations (50%) and on the progress-monitoring project (50%). To pass the course, a passing grade on all three components must be obtained. Only in exceptional cases (and in discussion with the instructor) can partial grades be carried over to subsequent years.
Timetable
For the timetable of this course please refer to MyTimetable
Reading list
Hosp, M.K., Hosp, J.L., & Howell, K.W. (2016). The ABCs of CBM: A practical guide to Curriculum-Based Measurement (2nd ed.). New York: Guilford.
Study material will consist of recent book chapters as well as primary research articles from leading journals in education, psychology, and cognitive science.
Brightspace
Brightspace will be used during the course.
Registration
Education
Students must register themselves for all course components (lectures, tutorials and practicals) they wish to follow. You can register via My Studymap up to 5 days prior to the start of the course.
Exams
The exam of this course is the report for the progress-monitoring project. This means that you do not have to register yourself for this exam in My Studymap.
Contact information
During this course Professor Espin holds offices one hour immediately after classes. She can also be reached by email.