The Crisis in Higher Education

Source: Technology Review, Sep 2012

<the source article is worth reading in its entirety>

Classroom lectures are in general “boring,” he says, and taped lectures are even less engaging: “You get the worst part without getting the best part.”

While MOOCs include videos of professors explaining concepts and scribbling on whiteboards, the talks are typically broken up into brief segments, punctuated by on-screen exercises and quizzes. Peppering students with questions keeps them involved with the lesson, Thrun argues, while providing the kind of reinforcement that has been shown to strengthen comprehension and retention.

… social networks like Facebook provide models for digital campuses where students can form study groups and answer each other’s questions.

To fulfill their grand promise—making college at once cheaper and better—MOOCs will need to exploit the latest breakthroughs in large-scale data processing and machine learning, which enable computers to adjust to the tasks at hand. 

Delivering a complex class to thousands of people simultaneously demands a high degree of automation. Many of the labor-intensive tasks traditionally performed by professors and teaching assistants—grading tests, tutoring, moderating discussions—have to be done by computers. 

Advanced analytical software is also required to parse the enormous amounts of information about student behavior collected during the classes. By using algorithms to spot patterns in the data, programmers hope to gain insights into learning styles and teaching strategies, which can then be used to refine the technology further. Such artificial-intelligence techniques will, the MOOC pioneers believe, bring higher education out of the industrial era and into the digital age.

… the potential of educational software. Through the intensive use of data analysis and machine learning techniques, he predicts, the programs will advance through several “tiers of adaptivity,” each offering greater personalization through more advanced automation. 

In the initial tier, which is already largely in place, the sequence of steps a student takes through a course depends on that student’s choices and responses. Answers to a set of questions may, for example, trigger further instruction in a concept that has yet to be mastered—or propel the student forward by introducing material on a new topic. “Each student,” explains Kuntz, “takes a different path.” 

In the next tier, which Knewton plans to reach soon, the mode in which material is presented adapts automatically to each student. Although the link between media and learning remains controversial, many educators believe that different students learn in different ways. Some learn best by reading text, others by watching a demonstration, others by playing a game, and still others by engaging in a dialogue. A student’s ideal mode may change, moreover, at each stage in a course—or even at different times during the day. A video lecture may be best for one lesson, while a written exercise may be best for the next. By monitoring how students interact with the teaching system itself—when they speed up, when they slow down, where they click—a computer can learn to anticipate their needs and deliver material in whatever medium promises to maximize their comprehension and retention.

The advances in tutoring programs promise to help many college, high-school, and even elementary students master basic concepts. One-on-one instruction has long been known to provide substantial educational benefits, but its high cost has constrained its use, particularly in public schools. 

It’s likely that if computers are used in place of teachers, many more students will be able to enjoy the benefits of tutoring. According to one recent study of undergraduates taking statistics courses at public universities, the latest of the online tutoring systems seem to produce roughly the same results as face-to-face instruction.

The benefits of machine learning in education remain largely theoretical. And even if AI techniques generate genuine advances in pedagogy, those breakthroughs may have limited application. 

It’s one thing for programmers to automate courses of instruction when a body of knowledge can be defined explicitly and a student’s progress measured precisely. It’s a very different thing to try to replicate on a computer screen the intricate and sometimes ineffable experiences of teaching and learning that take place on a college campus.

… the essence of a college education lies in the subtle interplay between students and teachers that cannot be simulated by machines, no matter how sophisticated the programming.

Flipping the Classroom

The designers and promoters of MOOCs don’t suggest that computers will make classrooms obsolete. But they do argue that online instruction will change the nature of teaching on campus, making it more engaging and efficient. 

The traditional model of instruction, where students go to class to listen to lectures and then head off on their own to complete assignments, will be inverted. Students will listen to lectures and review other explanatory material alone on their computers (as some middle-school and high-school students already do with Khan Academy videos), and then they’ll gather in classrooms to explore the subject matter more deeply—through discussions with professors, say, or through lab exercises. In theory, this “flipped classroom” will allocate teaching time more rationally, enriching the experience of both professor and student.



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