Milrad, Spector and Daviden (2002) and de Jong and van Joolingen (2008)

Both of these two chapters named as modeling facilitated learning. There are many similarities between the two types of learning approaches, but there are also some differences.

First, both of them talking about two types of model based learning. In Milrad et al (2002), they described learning with model, and learning by model. In de Jong and van Joolingen (2008), they called the two types of model based learning as learning from models and learning by creating models.

Both articles talked about using computer simulation in model based learning.

In de Jong and van Joolingen (2008), they provided a definition of models at the beginning of the article. It is defined as a set of representations, rules, and reasoning structure that allow one to generate predictions and explanations (de Jong and van Joolingen, 2008, p.458). I believe it gave us a good starting point to understand about how they use models. Indeed, computer simulation seems fitting in this definition nicely.

de Jong and van Joolingen (2008) gave a little bit more description about their CoLab design (and it is nice to read their 2005 paper (van Joolingen et al, 2005)). They provided a very clear description of how CoLab support the scientific discovery learning process. Basically, CoLab supported the whole scientific discovery process, including data gathering, hypothesis testing, planning, and so on.

de Jong and van Joolingen (2008) quoted previous studies about the effectiveness of the model based learning. It should help students in conceptual understanding on science subjects, scientific reasoning, science knowledge, problem solving skills, modeling skills, and ability to perform far transfer. Therefore, it seems model based learning is promising. But why model based learning work? Milrad et al (2002) tried to provide a theoretical framework for such a case. They talked about situated learning and cognitive flexibility. I believe simulation model helps to bring learners closer to the context of activities. However, can other type of MBL also achieve the same effect? How close we need to bring the learners to the real activities in order for MBL works?

CFT (cognitive flexibility theory) emphasize multiple representation. However, it is not really clear that MBL has to include collaboration. Maybe this is why the authors created a new terminology, model facilitated learning. Unfortunately, both of those two papers did not really clear to how collaboration should be included in the model. In my understanding, they suggested that collaboration should be included, and provided some support for the collaboration. Should the teacher also involve in the collaboration as Clement (2008) suggested?

Seel (2003)

Seel's (2003) provides a very good overview of model-based learning and teaching. I think his focus in about "improving" of mental model. In figure 4 (p.70), Seel's description of the learning process have a final state. It is quite similar to what Clement (2008) described in his chapter. So, instructors are helping the learners to progress in mental model revision towards the final state.

I believe that final state maybe the goal of an instruction.

In the research scenario 1, Seels talked about presenting models to learners will affect their construction of mental model. I still haven't read the seminal work of Mayer(1989). I believe presenting model should affect learner's mental model construction, but how/in which direction?

This week, I read an article in Dr. Greene's class, Hall, Bailey & Tillman 1997 about student-generated illustration. Their claim is asking students to generate pictures is better than giving them the pictures when measuring their problem-solving ability. So, it sounds like that they asked the students to re-creating a model from text. Seel mainly talked about comparing students who build models with students who were given models. Actually, there can be a third case that the students generate models after they see some kind of model. Maybe that is cognitive apprenticeship when students see how the mentor created a model.

Victor

Casti (2008) & Clement (2008)

First Casti (2008) is a nice and short article that gave a brief review of what model is. It is important for us to understand what model is before we start. The discussion of predictive, explanatory, and prescriptive models is also interesting. Can we match those three kinds of model with empirical, conceptual, and design-based research?

The author suggested that is a "midway" between lecture style and discover learning. Actually, today when I teach a bible study class, I tried the method. I feel that people are more engaging, but it put a lot of cognitive load to the teacher because the teacher need to
1. understand the goal of the instruction very clearly; otherwise, discussion can be offtrack easily.
2. able to monitor the misconceptions.
3. able to come up with effective scaffold
4. as Clement (2008) suggested, the teacher need to hold off topics so that students can focus on one difficult topic at a time. Of course, it means that the teacher need to know what is difficult.

Of course, we may give teachers tool to guide them to decrease their cognitive load. For example, we can have some ways to guide the teachers to do preparation so that they have a list of misconceptions and corresponding scaffolding questions.

Actually, I am reading some ITS papers lately. It makes me thinking about whether those kindS of scaffolding can be performed by computer (or computer and teacher "work" together)?

Victor

last week reading

First a general question:
1) I am still working on last week's readings; while I continue to "train myself" to read 100 pages in 1 hour (I've been reading for about 9 hours total and have 2 articles left from last week), should I just leave the old readings behind or keep trying to catch up and fall farther behind?

Sterman 2002 thoughts: Complex systems are harder to understand (even with multiple mistakes on the same system in decision-making processes), and therefore harder to learn from. Well...yeah! This seemed to be the point of more than the first half of the paper. The strength of this paper definitely seems to be in Figure 8 (and surrounding text), where the "virtual world" is used as a "stepping stone" in understanding real world complex systems. Still though, the learner must experience real-world complexity to try out any new decision-making (mental models). I'd like to see/hear more examples about this (to see how it apply it to teaching of complex biological concepts).

Some thoughts from the 1st week readings

The readings of this week have a few major themes. First, our education system is not working. One interesting point that mentioned by Shaffer (2006) is that our educational system was geared to the industrial age, where we need people to perform some routine tasks accurately. So, the demand to do the “right” thing was important at that age. I don’t know whether our education was a result from the needs of the industry, but it is clear that the same type of educational outcomes that we are measuring today doesn’t work for the information age. We are now living in the information age (Akilli, 2007; Galarneau & Zibit, 2007; Prensky, 2007; Shaffer, 2006). The industry now looks for the 21st century skills such as knowledge sharing, creation, collaboration (Galarneau & Zabit, 2007).

The second theme of this week is that game and simulation can be a solution (Akilli, 2007; Galarneau & Zibit, 2007; Prensky, 2007; Shaffer, 2006). Even though some of the authors do not say game is the solution, the authors give a high hope for games to be a solution if it is effectively designed and implemented. Prensky (2007) even foresee that the education/training industry will move to be game-based soon because of the demand of the learners. In other words, because games are so good that people will want it. I may not agree with Prensky (2007) hope because innovation diffusion depends on a lot of factors. Theory of diffusion of innovations (Rogers, 2003) suggest that other factors such as compatiability with other technologies (technology in a loose sense that it does not have to be computing device), and complementarity with other technologies may also affect the diffusion of technology. Obviously, the improvement of technology seems like helping the game movement.

The third theme of this week is complexity. It is safe to suggest that the world is complex. Dorner (1987) provided a very good introduction of complexity. I see two main components in Dorner’s argument. (1) the cognitive components which suggest that human’s mind has limitation to deal with the complexity (2) the affective components, where fear of failure is a key driver to prevent people to deal with complex phenomena effectively. In Dr. Ge’s class, we examine computer as cognitive tools to support cognitive processes. However, I also believe that computer may also support the affective side of the equation. It will be interesting to examine any interaction effect between those two dimensions.

The two books by Shaffer (2006) and Prensky (2007) are more practitioner oriented. They have a lot of good observations, but I haven’t found good support from both books yet. Maybe I can find those in the later chapters. Anyway, they did raise many good issues that game-based design researchers should pay attention to. For example, Prensky (2007) and Akilli (2007) both talked about the addictiveness of game. Yes, game is additive. I was addicted to game, too. But, why it is addictive?

Another good point that Prensky (2007) observed is that gamers have expectations on the games. They are intentional which is an important concept for learning under constructivism. Gamers expect game is better than their previous game. They expect the graphic is better. They expect to network with other people. They expect to play hard. Actually, many of the readings claim that game provide the motivation for the learners to engage, and learning will happen if the game is designed effectively.

I applaud Akilli’s effort to start understand what is game and how can it be implemented in educational context (Akilli, 2007). It is important to understand what is a game, and what we want to get out of the a game before we try to understand how we utilize game as a tool to assist learning. The authors did summarize some definitions of games from other people, and he adds that game should be fun and creative. However, I could not find a working definition for game yet. Actually, it is relatively hard to measure creativity. Fun is a subjective measure, which can be influenced by the society. For example, some boys may find basketball is a fun game, but some other boys may find basketball boring, and they like to play card game instead. In other words, people define fun differently. Actually, this can be an issue for game-based learning if fun is a pre-requisite of a game.

Akilli (2007) uses a terminology called “game-like learning environment”. I find trouble with this terminology when we do not have a working definition of a game. Actually, from the readings we have this week, I feel that (it may not be true) people look at game as a black box. Anything that has some sort of game characteristics can fit into the game-based learning (or we just called them game-like learning environment since we know it is not really a game). Instead, I suggest that we need to examine the components of the game, and match those components with the expectation that we find beneficial to educational environment. Motivation and engagement are two of those big sellers for game-based learning. I think they are some legitimate constructs that we can examine as moderating variables or dependent variables. Also, implementation issues can be factors which affect the success of the game/simulation.

Finally, I have a personal belief that the world is not always fun. So, we should teach our kids that we try to make things fun, but we will also work hard on the boring stuffs. For example, a high school teacher may love to teach, but he/she may not like to deal with parents. A college professor may love to do research, but he/she may not like to deal with administrative issues and get funding. Life is full of examples like that. We, as responsible adults, we need to deal with boring stuffs effectively so that we have energy to work on the fun stuffs. Therefore, I think game-based learning is a good idea, but too much of it may discourage students to work on something that is not really fun, but maybe necessary.

vibes about complex systems

i have vibes about researching more about complex systems...how to make complex systems as simple as possible...this area of research seems to have potential for growth.

Also noted from Sterman and Sabeli, research in complex system crosses various interdisciplinary bounderies. I wondering how to widen knowledge across domains...maybe collaborative research with other disciplines...

Shaffer, Intro & Chapter 1; What is a game?

What Shaffer wrote about in his introduction, resonated with me. Mostly, that teaching "content-only" in schools is no longer appropriate with increased outsourcing for "industrial" skills in America (those practiced through memorization, trained skills, standardized tests). It was necessary when schools were first created during the Industrial Revolution. But now, we need to find a way to teach students to practice innovation, creativity, and adaptability to new technologies, information, and procedures. He claims one solution is through the use of epistemic games.

Chapter 1 of Shaffer's "How computer games help children learn" gets into more detail on what a "game" really is. He explains that "fun" and "competition" aren't defining characteristics of a game (though they may be a part of games). Instead a game is an activity in which players are assigned "roles" which are governed by rules (as is the backdrop of these roles)(p23). Later Shaffer refers to these as role-playing games (but doesn't tell us what other kinds of games there are, and if they have different defining characteristics!). Role-playing games allow students to begin forming subject-specific epistemologies (ways of thinking).

In order to play these games, student learn content...but that is not the point. The point is that they are learning to think more creatively, and practice being a "professional" in some field. In the process they should be thinking critically and creatively, forming epistemologies about the subject-at-hand. Note that technology has not even been mentioned yet...this is just about games (p38)! However, I think we'd all agree that technology can give an easier platform both to present, play, and collaborate on games AND to allow students to become familiar with new/different technologies to expand their ability to adapt in the future.

My only major critique of Shaffer, so far, is a seeming contradiction he makes. On page 8 he states that what we do with technology is less important than the fact that we're just using technology; however, he goes on to state that gaming doesn't require technology, and that in using games we must be careful to set them up to encourage learning (pp39-40). Seem odd to anyone else?

Excellent conceptual paper on learning in complex systems

I love this paper by Sterman. The impediments to learning in the real world provide me a better appreciation on the complex problem solving...so many dynamic variables to consider and these variables are casual. Nonetheless, system thinking is key to solve complex systems.

Fortunately, the use of virtual worlds and simulation provide means to model the complex system in a controlled environment.

looking forward to the applications of virtual world to see how things are implemented.

Low performing students and self-reflection

According to Dorner (1987), it is interesting to know there is a correlation between low performing subjects and self-reflection. these subjects do not reflect as often and may lead to cognitive emergency reaction, which I would interpret it as pressing the PANICK button. I did appreciate Dorner for describing its symptons and consequences though.

Since this is a complex problem solving experiment, I am curious on how he collect the data? Using think aloud protocols or focus groups or ???

Check out Nero!

Just in case anyone is still reading, I figured I'd post this.