My Teacher is a Computer?

By Keith Millis, guest contributor

According to a scene from the 2009 movie Star Trek, young Vulcans experience a system of education quite different from our own. Instead of learning from “teachers,” they are instructed in pods surrounded by computer displays that quiz the students on Vulcan virtues, such as logic and morality.

Back on Earth, we do not have Vulcan pods. But the technology that is being used and developed might be getting close.

Source: Thinkstock

In fact, many researchers, educators and computer scientists point out many virtues of individualized instruction and assessment using advanced computerized learning environments.

In some cases, such as with intelligent tutoring systems (ITS), the environments provide personalized instruction on a one-on-one basis. Like human tutors, these systems present problems, hints, feedback and direct instruction when needed.

There are also digitally-based gaming programs that teach. Known as “serious games,” they incorporate features such as competition, challenges, points and storylines in an attempt to increase engagement and persistence.

Other platforms and types of advanced learning environments are being developed whereby students can be joined or consulted online by other human students, and in some cases, virtual students.

These technologies are usually meant to supplement activities in the classroom, rather than to replace teachers. So, if you are teacher, you can stop holding your breath. It will be a while until you are replaced, if ever.

But keep in mind that computer-based tutoring and serious games are effective. In fact, intelligent tutoring systems almost match the level of effectiveness of human tutors (vanLehn, 2011).

Some advantages to using advanced computer-based learning environments:

• Accessibility and cost. Many of the programs are available online 24/7, as long as there is some connection to a computer. In contrast, most instructors and human tutors are only available during prescribed times. And note that hiring expert tutors can put a serious dent in your pocketbook.
• The ability to diagnose individual strengths and weaknesses. Learning technologies remember what the student knows and what the student needs to work on. In contrast, instructors typically tend to remember what the class on the whole knows, rather than any one student.
• Tailored instruction. Because they create a model of the student’s knowledge from his or her performance, advanced learning technologies carefully pick problems or examples that will target specific deficits of the targeted skill or knowledge. Classroom instructors may not have the time to diagnose every nook and cranny of the curriculum that a particular student needs help on.
• Sensitivity to emotions. Because learning can be emotional (remember your frustration, confusion, boredom or delight in that one science class?), technologies are being developed that recognize the student’s emotion through the dynamics of the computer-human interaction, facial expressions, and posture recognition and then use that information to help the student move productively forward (D’Mello & Graesser, 2012).
• Learning through dialogue. Some technologies allow the student to learn from visually animated “agents” (e.g., people, talking heads) that can hold a conversation (Graesser, Li, & Forsyth, 2014). Learning through conversation is very natural, going back to the dawn of humankind (and perhaps Vulcan-kind, as well).

Now some reasons for caution:

• Fun does not always lead to increased learning. In many serious games, there is a narrative or storyline that accompanies the learning activities. While narratives can increase engagement and persistence, narratives may also distract from learning (Adams et al., 2012). More research is needed to figure out when narratives help and hurt engagement and learning.
• Motivation is tricky. On the outcome of several published articles, Pieter Wouters and colleagues (2013) found that serious games do not significantly increase motivation to learn over conventional teaching methods. This was a bit of a shocker to find out because increasing motivation is a big reason why instructors adopt serious games in the first place. Wouters points out that having the instructor (rather than the student) choose the game might curtail the students’ intrinsic motivation.
• Pizzazz may not lead to increased learning. Added sounds, visuals, animations and words meant to elicit interest can also lower learning. Richard Mayer and colleagues (2014) have documented many ways in which multi-media may affect learning, both positively and negatively. For example, the added elements can split the attention of the student. Anyone who has listened to a PowerPoint lecture may have experienced a split-attention effect: Do you follow the words on the presentation screen or listen to the speaker? Following and comprehending both at the same time is tough, and the same principle applies to many multi-media situations when they are not designed properly.
• Not all subjects are available. Although there are successful examples of Intelligent Tutors, such as Carnegie Learning’s Cognitive Tutor© that teaches math to middle-school students (Anderson et al., 1995), many others are not commercially available. One reason is that companies that typically provide educational materials, such as textbook companies, are not always best equipped to deal with the development, testing and upkeep of digital media products.

So, if you are considering using educational technologies, either as an instructor or as a student, make sure to do your homework as to whether there is empirical research validating its effectiveness. If there is none, then be cautious about adopting it.

Except, that is, if you are a young Vulcan. The high council may not let you out of your pod.

Keith Millis, Ph.D., is a professor of psychology at Northern Illinois University. He was the project director for Operation ARA (Millis, Graesser, & Halpern, 2014), a serious game that teaches research methods.

References

Adams, D.M., Mayer, R.E., Koenig, A., Wainess, R., & MacNamara, A. (2012). Narrative games for learning: Testing the discovery and narrative hypotheses. Journal of Educational Psychology, 104, 235-249.

Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutor: Lesson learned. The journal of the learning sciences 4(2): 167–207.

D’Mello, S. K. & Graesser, A. C. (2012). AutoTutor and affective AutoTutor: Learning by talking with cognitively and emotionally intelligent computers that talk back. ACM Transactions on Interactive Intelligent Systems, 2(4), 23: 1-38.

Education Essential Reads

Which Trendy Teaching Techniques Actually Work?

Why Students Should Resist Using ChatGPT

Graesser, A. C., Li, H., & Forsyth, C. (2014). Learning by communicating in natural language with conversational agents. Current Directions in Psychological Science, 23, 374-380.

Mayer, R. E. (2015). The Cambridge handbook of multimedia learning. Cambridge University Press: New York.

Millis, K., Graesser, A., & Halpern, D. (2014). Operation ARA: A serious game that combines intelligent tutoring and learning principles to teach science. In V. Benassi, C.E. Overson, & C.M. Hakala, (Eds.) Applying the Science of Learning in Education: Infusing psychological science into the curriculum. Retrieved from the Society for the Teaching of Psychology web site: http://teachpsych.org/ebooks/asle2014/index.php

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221.

Wouters, P., van Nimwegen, C., van der Spek, E.D., & van Oostendorp, H. (2013). A meta-analysis of the cognitive and motivational effects of serious games. Journal of Educational Psychology, 105, 249-265.