MASSIVE OPEN ON-LINE COURSES IN ENGINEERING N. AL-MUTAWALY, MOHAWK COLLEGE, HAMILTON, ONTARIO M. PICZAK, MOHAWK COLLEGE, HAMILTON, ONTARIO 1

MOOCs in Engineering  Remote delivery/Distance/Blended  Asynchronous/Synchronous  MOOC:  M: “massive” class cohorts  O: “open” to anyone often with no prereq’s.  O: “on-line” delivery typically asynchronous  C: “courses” collection of instructional content 2

Success In Traditional Classroom 3 Many factors predictive of academic success: Self efficacyTutoring Remaining on taskAcademic advice Instrumentality perceptionsPersonal life factors Vocational intentCollegiate career centers Co-op educationCommunication Feedback: frequency/qualityClass size

Attractions  MOOCs attractive when:  Class sizes traditionally large = 35,000  Limited instructor contact  Paper & pencil demonstration of cognitive ability suffices  Tuition costs = obstacle  Students spatially separated from delivering institution  Extending the brand beyond local area  Glitter of windfall cashflows  Many assume F2F simply translates into MOOC  Capital cost avoidance for bricks and mortar  Democratization of education  Elevating level of human capital on planet 4

Weaknesses  Pedagogical approach – fact accumulation, problem depth, discussion/argument  Class size – delivery, evaluation, drop out rates, influence of one person on millions  Achieving learning outcomes (design, hands-on skills/labs)  Isolation & lack of attachment to course, campus, institution  Long term ability to monetize ‘free’ education  Not generally favoured for any number of ‘pet’ reasons 5

But If Others Are Doing It, Then… 6

Platforms 7

MOOCs by Stakeholder 1. Accrediting Bodies: Design elements/skills, labs 2. Institutions: financial potential 3. Students: flexibility/convenience, unlimited access to material, global connections, pride in presenting credentials from world renowned institutions 8

Empirical Assessment of Completion Rates  Disciplines: engineering, management & ‘other’ courses  Dependent variable: completion rates, dataset n = 111  Independent variables:  Class size  Academic discipline  Evaluation method  Delivery platform  Course duration  Data from Ph.D. thesis; adopted by British Department of Innovation & Skills, European Commission 9

Deficiencies On Dependent Variable  Completion rates = primary dependent variable  Free riders, stay-at-homes, seeking alternate information source  High no-show (40-50%)  New metrics yet to be constructed 10

Study Findings Statistic Full Eng. Mgmt.Other Courses Students Class size Max Min Compl’n Rate 14.0%12.7%19.3%13.3% Std. dev. 11.3% 10.7% 11.9%11.3% Min..7%.9% 4.4%.7% Max. 52.1% 35.8%41.6% 11 Completion Rate = f(class size, discipline, evaluation, delivery platform, duration)

Class Size & Academic Discipline StatisticFullEng.Mgmt.Other R2R Adj. R SE Sign. F*Yes Intercept* Coef.* n

Course Duration StatisticFullEng.Mgmt.Other Courses Duration (wks) Std. dev R2R Adj. R SE Sign. F**Yes Intercept** Coef.**

Conclusions & Discussion  Study completion rate (14%) in agreement with others (4 -15%)  For completion rates:  Class size: significant impact (high 1-R 2 )  Discipline: differential impact (not significant)  Evaluation method: no significant effects  Platform: no significant effects  Course duration: negatively proportional across all subjects (significant) 14

Recommendations for Engineering 1. MOOCs may lend themselves to engineering courses 2. Basic engineering courses, consider experiment kits/simulation software to capture lab components 3. Advanced engineering labs, consider compressed lab formats 4. To satisfy accrediting bodies, adopt MOOCs CAREFULLY to ensure all competencies satisfied 5. More MOOC research be conducted 15

At The End Of The Day… “ one Coursera course = more students than I would teach in a lifetime… ” 16

MASSIVE OPEN ON-LINE COURSES IN ENGINEERING N. AL-MUTAWALY, MOHAWK COLLEGE, HAMILTON, ONTARIO M. PICZAK, MOHAWK COLLEGE, HAMILTON, ONTARIO The end Questions 17