This study shows that “machine-guided learning” software is just as effective as “human-guided learning” (also known as teachers):
In experiments at six public universities, students assigned randomly to statistics courses that relied heavily on “machine-guided learning” software — with reduced face time with instructors — did just as well, in less time, as their counterparts in traditional, instructor-centric versions of the courses. This largely held true regardless of the race, gender, age, enrollment status and family background of the students.
The study comes at a time when “smart” teaching software is being increasingly included in conversations about redrawing the economics of higher education. Recent investments by high-profile universities in “massively open online courses,” or MOOCs, has elevated the notion that technology has reached a tipping point: with the right design, an online education platform, under the direction of a single professor, might be capable of delivering meaningful education to hundreds of thousands of students at once.
The new research from the nonprofit organization Ithaka was seeking to prove the viability of a less expansive application of “machine-guided learning” than the new MOOCs are attempting — though one that nevertheless could have real implications for the costs of higher education.
The study, called “Interactive Learning Online at Public Universities,” involved students taking introductory statistics courses at six (unnamed) public universities. A total of 605 students were randomly assigned to take the course in a “hybrid” format: they met in person with their instructors for one hour a week; otherwise, they worked through lessons and exercises using an artificially intelligent learning platform developed by learning scientists at Carnegie Mellon University’s Open Learning Initiative.
Researchers compared these students against their peers in the traditional-format courses, for which students met with a live instructor for three hours per week, using several measuring sticks: whether they passed the course, their performance on a standardized test (the Comprehensive Assessment of Statistics), and the final exam for the course, which was the same for both sections of the course at each of the universities.
The results will provoke science-fiction doomsayers, and perhaps some higher-ed traditionalists. “Our results indicate that hybrid-format students took about one-quarter less time to achieve essentially the same learning outcomes as traditional-format students,” report the Ithaka researchers.
The robotic software did have disadvantages, the researchers found. For one, students found it duller than listening to a live instructor. Some felt as though they had learned less, even if they scored just as well on tests. Engaging students, such as professors might by sprinkling their lectures with personal anecdotes and entertaining asides, remains one area where humans have the upper hand.
But on straight teaching the machines were judged to be as effective, and more efficient, than their personality-having counterparts.
Some, are not so welcoming:
In terms of instructor compensation, the researchers estimated, a machine-guided course featuring weekly face-to-face sessions with part-time instructors would cost between 36 and 57 percent less than a traditional course in which a full professor presides over each 40-student section; and it would cost 19 percent less than if a single full professor gave one lecture to all sections before breaking them into smaller discussion groups led by teaching assistants.
The perennial fear among faculty is that the growing credibility of automated teaching software could tempt administrators to replace instructors with robots. But Bowen and company make the case that automated teaching software could enable colleges to save money without firing tenured professors.
A hybrid teaching model could shift a great to deal of the teaching burden from tenured professors to teaching assistants and support staff, they explain. That could allow institutions to enroll more students without hiring an equal proportion of expensive tenured faculty. “Recruitment costs may thereby be reduced along with compensation costs per student, and debates over maintaining commitments to existing faculty are avoided,” the authors write.
Read more: http://www.insidehighered.com/news/2012/05/22/report-robots-stack-human-professors-teaching-intro-stats#ixzz1vdnGXjJ4
Inside Higher Ed