Modeling Systems
Systems
The Beginning of the chapter stresses on the importance of CONTENT.
Modeling systems with concept maps:
- Concept mapping enables learners to identify components and allow them to link systemic interactions between those said components in order to explore relationships and interaction
- However, concept maps can NOT represent the dynamic nature of interactions such as cause and effect. Systems modeling tools are needed for such a venture.
Modeling Systems with systems modeling tools
Dynamic systems models are created by by describing the quantitative relationships among the components, then testing them by running the models.
- It is essential to question student models so they can make necessary adjustments.
- The teacher needs a well-developed conceptual framework to question the validity of student models.
Modeling systems with spreadsheets
Spreadsheets are effective for illustrating dynamic interaction among system components.
Chapter 6
The beginning states "The more accurate a learner's representation of a problem, the better the learners solution is likely to be.
Spreadsheets:
Can be used to model nearly all phenomena in math and science.
Simply change the values of the variables and the other variables in the figures change.
Expert systems:
Solving problems requires some form of causal reasoning.
The more complex the problem is, the more sophisticated the causal reasoning must be.
Can be used to model nearly all phenomena in math and science.
Simply change the values of the variables and the other variables in the figures change.
Expert systems:
Solving problems requires some form of causal reasoning.
The more complex the problem is, the more sophisticated the causal reasoning must be.
Decisions are usually stated first.
Identify the decision factors in the form of questions that will be asked of the userThe designer writes the rules using IF-THEN (Boolean) logic to relate the decisions to the decision factors or questions
- To guide further interpretation of information about the problem
- to simulate the behavior of the system based on knowledge about the properties of the system
- to associate with and trigger a particular solution scheme
Qualitative data- Deals with descriptions, data can be observed but not measured, focus on the quality
Quantitative Data- Deals with numbers, data can be measured, focus on the quantity of data
Modeling Problems
with Databases
with Databases
- In solving problems domain knowledge should be well organized and accessible.
- The tables in a relational database can be searched together, to answer queries. Queries are formal searches of the database that can be predefined in the database program.
- The figure below shows the results of a formal query conducted in access, a database program.
Chapter 7
The Beginning of the chapter begins with "to be part of a culture, it is necessary to be connected to the stoires that about in that culture. Telling stories is a primary means for negotiating meanings, and stories assist us in understanding human action, intentionality, and temporality."
Stories relate to direct experience: Assume we learn from experiences and learn from stories of other people's experiences. Stories are rich, powerful formalisms, and store and describe memories.
Understanding what people know: Analyze their stories, Case Based Reasoning or CBR.
CBR- represents what people know
WHat people know is stored in memory as stories
Modeling Experiences in Databases
Collecting stories and experiences in a database is essential if you wish to recall the information for future reference. The database and an effective indexing system allows for fast recall of previous experiences.
Modeling Experiences with Hypermedia
Telling a story about someone or a group of people can be done by biography, documentary or ethnography. The hypermedia tool that was used to create the narratives was Storyspace. Storyspace is a generative, flexible writing environment that lets you collect, store, and experiment with your story ideas without having to worry about how they all fit together right away. The students enjoyed creating video, pictures and narratives to develop their biographical projects. It was interesting to know that narrative forms of representation are better retained and comprehended than declarative forms.
The people who investigate customs, habits and social interactions are ethnographers. In order to capture multiple perspectives and data, Riki Goldman Seagull developed Learning Constellations. Her concept knowledge base allowed students to collaborate and create a multimedia culture in the classroom. Although telling stories is a vital part of human interaction, it can be an effective learning tool as well.
The people who investigate customs, habits and social interactions are ethnographers. In order to capture multiple perspectives and data, Riki Goldman Seagull developed Learning Constellations. Her concept knowledge base allowed students to collaborate and create a multimedia culture in the classroom. Although telling stories is a vital part of human interaction, it can be an effective learning tool as well.
Chapter 8
This chapter details different models of cognition and encourages the use of Mindtools for modeling meta-cognition. In simple terms, metacognition is thinking about thinking. In a more definitive description of the term, metacognition refers to higher order thinking which engages active control over the cognitive processes engaged in learning. Metacognition occurs when “one must stand back from a particular mental activity and comment on the activity rather than participating in it” (Reisberg, 1997). These processes are important skills that are required for most higher order thinking processes.
Constructing Cognitive Simulations
Cognitive simulations are otherwise known as models of cognition. “Cognitive simulations are runnable computer programs that represent models of human cognitive activities” (Roth, Woods, & People, 1992, p. 1163). The main rationale of cognitive simulations is to regard mental constructs for analysis and theory building.Cognitive simulations were created by Newell and Simon at some stage in the information processing revolution in psychology. In 1972, computers had just started to be utilized to illustrate how humans actually processed information. The development of an operational computer model of those processes seemed to be the most scientific way to simulate the processes. This cognitive simulation represent the link between psychology and computers. While the computers and tools that the psychologists and computer scientists used in order to create these simulations were very difficult and computer intensive, the Mindtools in this book are easier to utilize to construct the cognitive simulations.
In the beginning, cognitive simulations were utilized for the designing and implementation of intelligent tutoring systems. These tutoring systems are meant to analyze and identify a student’s understanding of a subject and adjust the instruction to the student’s capabilities
In the beginning, cognitive simulations were utilized for the designing and implementation of intelligent tutoring systems. These tutoring systems are meant to analyze and identify a student’s understanding of a subject and adjust the instruction to the student’s capabilities
Jonasson describes his own experience with psychology students and their task to build a rule based cognitive simulation. The students began by identifying a number of learning strategies such as
Recall
Organization
Integration
Elaboration
Students learned that metacognition is tied to the learning need. Therefore any abstract model of metacognition must also be tied to a particular learning need.
Recall
Organization
Integration
Elaboration
Students learned that metacognition is tied to the learning need. Therefore any abstract model of metacognition must also be tied to a particular learning need.
The following rule base was established that takes into account learner and task characteristics:
1. Processing depth
2. Learner characteristics (what do they know, what do they need to know, learning style)
3. Difficulty level of the task
4. Support strategies (eg. energy level, interest level, place of study, perception of ability).
Plugging in the above variables elicits a set of results that will best create the learning outcomes desired.
USING SYSTEMS DYNAMICS TOOLS TO SIMULATE THINKING
The Stella Model of motivation correlates motivation and success and failure considerations. The model considers level of satisfaction, expectancy levels, relevance, effort, and performance. This model enables testing of current models. The model can be repeated and used to measure the probability of failure, incentive values and can be visible through graphs. In addition to this the Stella model can be used to compare models as well.
1. Processing depth
2. Learner characteristics (what do they know, what do they need to know, learning style)
3. Difficulty level of the task
4. Support strategies (eg. energy level, interest level, place of study, perception of ability).
Plugging in the above variables elicits a set of results that will best create the learning outcomes desired.
USING SYSTEMS DYNAMICS TOOLS TO SIMULATE THINKING
The Stella Model of motivation correlates motivation and success and failure considerations. The model considers level of satisfaction, expectancy levels, relevance, effort, and performance. This model enables testing of current models. The model can be repeated and used to measure the probability of failure, incentive values and can be visible through graphs. In addition to this the Stella model can be used to compare models as well.
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