3-D technologies are particularly helpful in encouraging and supporting a social environment or the social aspects of learning. This is because members feel as if they are "in" the community b/c of the detailed visual aspects of this technology. Members can create avatar's or computer simulations of themselves and the community can take the form of lakes or buildings rather than computer screens with words flashing across them. This type of technology would be useful in lieu of a traditional discussion board or chat room to promote continued participation and interest in the site. 3-D technologies can be used for large or small groups in both synchronous and asynchronous settings.
The method used by ASU IT was very interesting and did seem to promote social interaction more than any other model that I have read about thus far. The site is open to all students taking the same class, even if they are in different sections of the class. Previous students were also encouraged to participate and some did. This was an interesting approach b/c it enabled students to form bonds and to have communications with people (fellow students and instructors) that they would not have had access to in different circumstances.
This chapter identified many theories that support learning as a social process. If learning is a social process, then 3-D technologies would certainly prove invaluable tools for facilitating social interactions and the development of social ties or relationships b/w members. Theories that support the use of 3-D technology in learning include:
- Situated learning (Lave & Wenger)
- Sociocultural (Vygotsky)
- Cognitive apprenticeship (Brown, Collins, & Duguid)
- Constructivism (Bruner)
- Dewey's theory
All of these theories are used to form the cognitive scaffolding for 3-D technology in learning. Students become active participants, compelled to communicate with each other to complete naturally occurring tasks. It is through these interactions that meaningful learning occurs.
"Scientific Discovery Learning with Computer Simulations of Conceptual Domains"
This article discussed the difficulties encountered when using computer simulations in learning and methods of instruction that could be utilized to combat these difficulties thereby having a positive affect on learning.
The primary student problems or barriers to "scientific discovery learning" as identified by the authors include:
- Choosing only "safe" hypotheses
- Inability or great difficulty forming a hypothesis from given or collected data
- Poorly designed experiments that yield no data (may not even relate to the hypothesis)
- Inexperienced and inefficient experiment behavior (may not use of even know of all available tools and experiment designs)
- Tendency toward confirmation bias (work to prove hypothesis, discredit or disregard any data that does not prove hypothesis)
- Tendency to use an engineering experiment design (try to create the desired outcome rather than test the current hypothesis)
- Difficulty interpreting data (I have the info.....What do I do now? What does it mean?)
- Students have difficulty regulating their own learning process (They need structure. Do "A" first, next complete "B", etc..)
Instruction methods recommended to help meet the needs of students include:
- Providing access to "just in time" information during simulation. (This is the type of information used in Chapter 12 of CSCL 2 as well.)
- Provide additional assignments that support the instructional goal for the simulation. This can include guided questions or games. It was reported that students who learned using games ask more "what if" and "how" questions.
- If the model that you are presenting is complex, the authors recommend using model progression. Model progression is similar to constructivism in that you guide learners through simulation that begins at the novice level and builds in complexity to expert level.
- Structure the learning environment for the student. Break things into small "tasks" and then guide them through the learning experience. This is done by prompting them with questions or new "tasks" based on previous task.
Successful implementation of simulation leads to scientific discovery learning. Knowledge gained during discovery learning is instinctual and remembered longer than passive learning. This means that the student is more likely to be able to apply the knowledge gained in "real world" situations.
Jammie
