The HG2S Training Blog

The Bandwagon

If you are thinking of using social media in a class to help build useful collaborative connections, retire the fears of shy students and introduce the same engagement you see in sites like Facebook, think again.  A recent study by the Lab for Social Computing at Rochester Institute of Technology suggests that the use of social media in classrooms might yield little effect in improved communications and enhanced connections between students. The study into the effects of social media was conducted as part of a course on the use of social media and tools. It included contributions from online learning and course management systems and discussion groups that were proposed to enhance instruction, improve communication and facilitate connections between the students and course content. The results indicate that poor social acumen in the face-to-face interactions might be mirrored in the (more) virtual social medium. What’s more, echoing teacher and educational social media researcher Michael Wesch, the RIT study suggests that the educational use of social media may have to be learned:

“…the educational use of social media may not counteract poor social connections that are seen in face-to-face communication or elicit the same impacts seen in the use of social media sites such as MySpace and FaceBook.”

Researcher and team leader Susan Barnes comments on the hopes and goals of social media in the educational environment relative to her team’s findings:

“Many social media advocates have argued that the use of these tools in classroom settings could greatly enhance interaction and learning and assist shyer, more reserved students in becoming more involved, as has been seen in other online environments. However, our findings show that the incorporation of social media had no measurable impact on social connections, to the point that students did not consider other members of the class to be part of their social network.”

The RIT research team plans to expand the study to consider different educational formats and additional social media applications in an effort to determine the effects and differences of social media from traditional classrooms. The intent is to help educational planners and instructional designers better use social media in course development and delivery.

“The issues surrounding poor social network construction within online educational environments points to greater opportunities to examine how technology and mediated software can be better designed to suit the types of communication and interactions desired by our students.”  – Christopher Egert, co-author

References.
Jacobs, Stephen, Egert, Christopher A., Barnes, Susan B., “Social Media Theory and Practice: Lessons Learned for a Pioneering Course,” 39th ASEE/IEEE Frontiers in Education Conference, T4J-1, October 18 – 21, 2009, San Antonio, TX.

Study Examines Use of Social Media in the Classroom

Weighing in on the side of blended learning, Dr. Caroline Haythornthwaite of the Graduate School of Library and Information Sciences, University of Illinois at Urbana-Champaign, states that e-Learning may be at its best when used as a tonic to the traditional classroom.

“Compared to the more traditional educational paradigm – the broadcast model, where knowledge is delivered from professor to student from on-high – e-learning turns teaching and learning into a shared endeavor.”

Citing a shift in dynamics between her online and brick-and-mortar classes, Haythornthwaite sees that online teaching offers more immediate and engaging interactions with the students:

“With the online classes, I interact with my students more frequently, dropping into asynchronous discussion daily for a half-hour or an hour. With my traditional classes, I might see them once a week for three hours. If there’s a news article I want my online students to read, I can post it and discussion can begin right away. With my classroom students, if I e-mail them an article on Tuesday and we meet for class on Friday, that’s one of many things we might discuss. The impact isn’t quite as immediate.”

In online instruction the roles of student and teacher are modified. The teacher moves from pundit to facilitator and the student is urged to assume a greater active role in his or her tuition.

“Since there’s an emphasis on more learner-centric activities than traditional lecture-based classroom learning, the teacher is more of a facilitator in an online classroom. Not only does that enhance the collaborative nature of online learning, it also motivates students to be much more engaged and to take more responsibility for what they’re learning.”

Haythornthwaite doubts that e-Learning will (or should) replace traditional classroom instruction, asserting instead that it is best used as a complement to lecture and demonstration. Noting the move to open source course materials at places like MIT, Haythornthwaite says:

“No one stopped going to class when all that material was posted. It simply changed the delivery method and broadened the scope of knowledge available.”

References.

Haythornthwaite’s Blog (includes many research papers)

E-Learning can have positive effect on classroom learning, scholar says

Cutting Class – Online vs. Classroom Learning

I can’t say whether the only course I’ve taken in programming was taught well. This is partially the case because it was so long ago and looking back on it it’s doubtful that anyone had an idea about how to teach such a new subject. It seems in retrospect that the professors and graduate students of that era were trying to figure out how to program themselves, let alone teach programming to undergraduates. To give you an idea, the language I learned in class was something called FORTRAN.

Since then I have had to learn (to some degree) about a dozen programming and scripting languages. Some were for application development, some were for web development, others were for database systems, but all were a hard-fought climb up a learning curve of an unnatural new literacy. Since I am not a real “computer person” I have had to learn to program for practical reasons such as building new tools or to complete a project. This is to say, I have had to start learning new languages from the position of a neophyte – someone without much formal knowledge or skill – who nonetheless had a practical goal or objective in mind.

Often when working around computer scientists and engineers who program for a living, I would ask how to best go about learning programming. Invariably I was told that the best (and only) way to learn to program was to program. I think this was the result of my colleagues early experience and education. They read books on the syntax and rudiments of the language in question and started in on cobbling together simple lines of code that eventually grew to more and complex routines until they achieved a modest proficiency in the language and it quirks. And so did I.

As things progressed, and I added more computer languages to my list of things to learn, I started to suspect that I could climb the learning curve a little faster if I read lots of programming examples to get a good sense of the everyday grammar of the language and learn some of the colloquial shortcuts employed by experienced users. In a sense I began to suspect that learning a programming language was much like any other foreign language.

It seems professionals in the field of computer science are having some of the same concerns. Professor Mark Guzdial, of the Georgia Institute of Technology, writing in the blog of the Communications of the ACM, lays it on the line in the title of his post:

“How We Teach Introductory Computer Science Is Wrong.”

Basing this conclusion not only on his own experience but also on results from several researchers, Guzdial questions whether extensive use of programming exercises are the best path to teaching programming to introductory learners. That is, is it best to teach problem solving by problem solving?

Guzdial starts his critique of computer science instruction by citing research in mathematics education by Sweller and Cooper (1985). In it, Sweller and Cooper compare two groups of students both of which are shown two worked examples in algebra. An experimental group is given eight more completely worked out examples in algebra. The control group gets the same eight problems to work out themselves. Not surprisingly the control group takes five times longer to complete their assignment. Next, both groups get a new set of problems to solve. Ready for the ta-da? Drum roll please….

“The experimental group solves the problems in half the time and with fewer errors than the control group.” – Guzdial, 2009

In other words, the work-it-out-for-yourself problem solving approach was less effective by a long shot. And, as an aside, it should be said that this approach to instruction is common not only in computer science courses but also in subjects like mathematics, physics, chemistry and engineering.

Other work by researchers Kirschner, Sweller and Clark (2006) and Kalyuga, Chandler, Tuovinen and Sweller (2001) comment on this effect and help explain where and when problem solving is superior to worked examples. Guzdial quotes Kirschner (1992) in summarizing the state of the problem:

“After a half-century of advocacy associated with instruction using minimal guidance, it appears that there is no body of research supporting the technique. In so far as there is any evidence from controlled studies, it almost uniformly supports direct, strong instructional guidance rather than constructivist-based minimal guidance during the instruction of novice to intermediate learners.”

Does this mean, as Marshall McLuhan was fond of saying, that “the whole fallacy is wrong?” Have we been sold down the river educationally where training in computer science, physical sciences, mathematics and engineering are concerned? Perhaps not. What the studies do suggest is that relying primarily on learn-programming-by-programming, work-it-out-for-yourself, minimal guidance methods are not well suited to introductory learners. These methods are, however, better suited to learners who have already acquired some background knowledge and are therefore a better fit to intermediate and advanced courses.

“What’s striking is that no one challenges [Kirschner, Sweller and Clark] on the basic premise, that putting introductory students in the position of discovering information for themselves is a bad idea!”  – Guzdial, 2009

That is not to say “never” of course. What the data are saying is that it’s not the best principal approach for beginners.

In hindsight the findings make perfect sense. My original intuition that learning a computer language is like learning a foreign language was not far off the mark.

The data suggest that for a beginner, learning to read before learning to write is a more effective approach.

References.

Kalyuga, S., Chandler, P., Tuovinen, J., Sweller, J. (2001), “When Problem Solving Is Superior to Studying Worked Examples,” Journal of Educational Psychology, 93(3), 579-588.

Kirschner, P. A. (1992), “Epistemology, practical work and academic skills in science education.” Science and Education, 1, 273-299.

Kirschner, P. A., Sweller, J., Clark, R. E. (2006), “Why Minimal Guidance During Instruction Does Not Work: An Analysis of the Failure of Constructivist, Discovery, Problem-based, Experiential, and Inquiry-based Teaching,” Educational Psychologist, 41(2), 75-86.

Sweller, J., Cooper, G. A., (1985). “The use of worked examples as a substitute for problem solving in learning algebra.” Cognition and Instruction, 2, 59-89.

In a note that could have been taken from one of Maria Montessori’s books, researchers in neuroscience, machine learning, education and psychology have convened to show that findings from a joint study suggest that “the prepared environment” might be supported by new scientific data.

“The ‘prepared environment‘ is Maria Montessori’s concept that the environment can be designed to facilitate maximum independent learning and exploration by the child.”

Terrence J. Sejnowski, Ph.D, researcher at the Computational Neurobiology Laboratory at the Salk Institute for Biological Studies and co-director of the Temporal Dynamics of Learning Center (TDLC) at the University of California, San Diego, echoes Montessori in his team’s findings. As quoted in Science Daily:

“To understand how children learn and improve our educational system, we need to understand what all of these fields [neurobiology, psychology, education, machine learning] can contribute. Our brains have evolved to learn and adapt to new environments; if we can create the right environment for a child, magic happens.”

The cross-disciplinary research points to a new science of learning that might influence the way classrooms are organized and run in the future. In particular, three guiding principles (or concurrent processes) emerge from the study:

1. Learning is computational
2. Learning is social
3. Learning is supported by neurological (perception-action) circuits

Research in machine learning and developmental psychology illuminate the computational complexity employed by learners who use statistical patterns and probabilistic models to infer rules of logic, relationships between words, syntax, and causal dependence between objects in the physical world.

Evidence that the three component processes happen concurrently is supported by the fact that learners do not calculate and compile statistical models of the environment indiscriminately but throttle the process using social cues from the people around them. Further, animal studies point to the presence of certain neurosteroids secreted during social interaction that promote learning.

Imitation also comes into play as a key factor:

“Imitation [presumably from others in the environment] accelerates learning and multiplies learning opportunities. It is faster than individual discovery and safer than trial-and-error learning.”

In essence, a social context fosters learning.

Brain circuits that support both actions and perceptions are directly involved with learning. As seen in language learning, for example, there is a complex mix of imitative, computational and articulatory processes that come into play as learning proceeds that might be further facilitated or enhanced at specific developmental periods. In general, neuroscientists have determined that there is considerable overlap in the systems brought into play during learning that support both perception and action. From Science:

“For example, in human adults there is neuronal activation when observing articulatory movements in the cortical areas responsible for producing those articulations. Social learning, imitation, and sensorimotor experience may initially generate, as well as modify and refine, shared neural circuitry for perception and action.”

Finally, experts in machine learning and artificial intelligence are taking advantage of the recent findings in social learning, computational modeling and the plasticity of the brain to design software that monitors and uses social cues and environmental factors to enhance learning. In the future this software may be used in tutorial programs or embedded in instructional robots that are specifically “tuned” to enhance teaching practices in classrooms.

References.

New Science Of Learning Offers Preview Of Tomorrow’s Classroom

Foundations for a New Science of Learning

New science of learning offers preview of tomorrow

From baby scientists to a science of social learning

Teaching College Courses Online vs Face-to-Face

A recent report by the US Department of Education will no doubt add fuel to an already raging debate over the virtues and deficiencies of online education. Evaluation of Evidence-Based Practices in Online Learning – A Meta-Analysis and Review of Online Learning Studies provides an extensive meta-analysis of data that compare the effectiveness of online versus traditional classroom approaches to instruction.

Designed to review over 1,000 empirical studies published from 1996 to 2008, the report focuses on studies that:

(a) contrasted an online to a face-to-face condition,

(b) measured student learning outcomes,

(c) used a rigorous research design, and

(d) provided adequate information to calculate an effect size.

The meta-analysis echoes results reported earlier, namely,

“… on average, students in online learning conditions performed better than those receiving face-to-face instruction.”

The authors of the report explain their findings by noting that:

“The difference between student outcomes for online and face-to-face classes—measured as the difference between treatment and control means, divided by the pooled standard deviation—was larger in those studies contrasting conditions that blended elements of online and face-to-face instruction with conditions taught entirely face-to-face. Analysts noted that these blended conditions often included additional learning time and instructional elements not received by students in control conditions. This finding suggests that the positive effects associated with blended learning should not be attributed to the media, per se.”

A paucity of data for K-12 is noted in the findings, leading the authors to caution readers about generalizing the results to that population.

A copy of the full report in PDF format can be downloaded here.