TY - CHAP
T1 - Measurement of quality of a course
T2 - Analysis to analytics
AU - Seanosky, Jérémie
AU - Boulanger, David
AU - Pinnell, Colin
AU - Bell, Jason
AU - Forner, Lino
AU - Baddeley, Michael
AU - Kinshuk,
AU - Kumar, Vivekanandan Suresh
N1 - Publisher Copyright:
© Springer-Verlag Berlin Heidelberg 2016.
PY - 2016
Y1 - 2016
N2 - Traditionally, the quality of a course offering is measured based on learner feedback at the end of the offering. This chapter offers a method to measure the quality of a course offering—continually, formatively, and summatively—using factors such as the quality of resources used, learner motivation, learner capacity, learner competency growth, and instructor competence. These factors are represented in a Bayesian belief network (BBN) in a system called MI-IDEM. MI-IDEM receives streams of data corresponding to these factors as and when they become available, which leads to estimates of quality of the course offering based on individual factors as well as an overall quality of the offering. Continuous, formative, and summative course quality measurements are imperative to identify weaknesses in the learning process of students and to assist them when they need help. This chapter professes the need for a comprehensive measurement of course quality and ensuing initiatives to personalize and adapt course offerings. It presents two case studies of this novel approach: first, measurement of the quality of a course offering in a blended online learning environment and second, measurement of the quality of training course offering in an industry environment.
AB - Traditionally, the quality of a course offering is measured based on learner feedback at the end of the offering. This chapter offers a method to measure the quality of a course offering—continually, formatively, and summatively—using factors such as the quality of resources used, learner motivation, learner capacity, learner competency growth, and instructor competence. These factors are represented in a Bayesian belief network (BBN) in a system called MI-IDEM. MI-IDEM receives streams of data corresponding to these factors as and when they become available, which leads to estimates of quality of the course offering based on individual factors as well as an overall quality of the offering. Continuous, formative, and summative course quality measurements are imperative to identify weaknesses in the learning process of students and to assist them when they need help. This chapter professes the need for a comprehensive measurement of course quality and ensuing initiatives to personalize and adapt course offerings. It presents two case studies of this novel approach: first, measurement of the quality of a course offering in a blended online learning environment and second, measurement of the quality of training course offering in an industry environment.
KW - Analysis versus analytics
KW - Blended online instruction
KW - Continuous assessment
KW - Course quality assessment
KW - Learning analytics
KW - Mixed-initiative instructional design evaluation model
UR - http://www.scopus.com/inward/record.url?scp=85030225260&partnerID=8YFLogxK
U2 - 10.1007/978-3-662-47724-3_11
DO - 10.1007/978-3-662-47724-3_11
M3 - Chapter
AN - SCOPUS:85030225260
T3 - Lecture Notes in Educational Technology
SP - 199
EP - 216
BT - Lecture Notes in Educational Technology
ER -