MI-IDEM: A model to evaluate instructional design using Bayesian Belief Networks

Kadi, Munir El; Kumar, Vive

doi:10.1109/ithet.2010.5480071

Cited by 2 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MI-IDEM is a system developed to assess the quality of any given course based on a combination of different learning and instruction-related data sets (El Kadi and Kumar 2010;Forner et al 2013). MI-IDEM uses feedback such as course exit surveys filled in by students enrolled in the course, log data from LMSs, feedback from teachers in the course, performance data from students' assignments and tests, and student postings in online discussion forums to assess course quality based on pre-defined evaluation criteria.…”

Section: Contribution Of Lambda To Mi-idemmentioning

confidence: 99%

“…' Bloom et al (1971). El Kadi and Kumar (2010) indicate that 'summative evaluations tend to provide answers regarding the worth of the overall course, after the learning experience is fully observed.' The goal of this enhanced version of MI-IDEM consists of a blend of three types of evaluations-continuous, formative, and summative.…”

Section: Mi-idem's Enhanced Bayesian Networkmentioning

confidence: 99%

See 1 more Smart Citation

Measurement of Quality of a Course

Seanosky

Boulanger

Pinnell

et al. 2015

The Future of Ubiquitous Learning

View full text Add to dashboard Cite

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.

show abstract

Section: Contribution Of Lambda To Mi-idemmentioning

confidence: 99%

Section: Mi-idem's Enhanced Bayesian Networkmentioning

confidence: 99%

Measurement of Quality of a Course

Seanosky

Boulanger

Pinnell

et al. 2015

The Future of Ubiquitous Learning

View full text Add to dashboard Cite

show abstract

“…While perceptions are important to ensure student satisfaction and hence their continuation of online education, they are subjective and need to be validated using actual performance and behaviors [8]- [12]. Recently researchers have proposed integrating multiple assessment instruments using Bayesian Belief Networks (BBNs) to improve the assessment of online course design [8], [12].…”

mentioning

confidence: 99%

“…BBNs provide a number of advantages over other methodologies for assessing course design [8], [12] - [21]. Since BBNs use a graphical representation, it is easy to understand the relationship between variables.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Assessing Design of Online Courses Using Bayesian Belief Networks

Forner

Kumar

Kinshuk

2013

2013 IEEE Fifth International Conference on Technology for Education (T4e 2013)

View full text Add to dashboard Cite

Even with recent research showing students enrolled in online courses are outperforming those in traditional courses, the need to assess the instructional design of online courses is greater than ever due to increasing enrollments and attrition rates that continue to be significantly higher than traditional courses. Most past research used surveys for course assessment, which measure only perceptions. Recently researchers have proposed more holistic assessments of online courses using Bayesian Belief Networks (BBNs) to integrate multiple assessment instruments. While the advantages of BBNs for educational assessments have been demonstrated, past research used only simulated data. This study furthers past research by testing the suitability of BBN-based methodologies for real-time assessment of online courses using larger data sets with missing and sparse data. Testing used actual course data from two undergraduate online Java programming courses. Past literature identified the creation of the conditional probability tables (CPTs) as the greatest challenge of using BBNs. This research also investigated the automatic population of CPTs via software. Tests revealed excellent performance with large data sets demonstrating the scalability of BBN-based assessment methodologies for online courses with large enrollments. With a well-designed network, most of the CPTs can be populated via software, significantly reducing the time and effort required to design and use the BBN. A number of recommendations to improve usability, performance, and accuracy of BBN-based course assessment methodologies are demonstrated. Future research should provide a comparative analysis between BBNs and other methodologies for the assessment of online course design.

show abstract

MI-IDEM: A model to evaluate instructional design using Bayesian Belief Networks

Cited by 2 publications

References 10 publications

Measurement of Quality of a Course

Measurement of Quality of a Course

Assessing Design of Online Courses Using Bayesian Belief Networks

Contact Info

Product

Resources

About