• What kinds of models or activity ideas from Van Lehn do you think would be important to incorporate when having students model Zika?
• Models we've already incorporated involve the agent-based system that NetLogo utilizes in their programming. NetLogo is a computer application that functions through fluency in its coding "language," which is a system that has a greater learning curve than other model exploration methods but a high payoff in comprehension. Scaffolding in the form of the NetLogo tutorials (which tell you what variables to use) could benefit students learning the program initially. Plotting a graph alongside the animation helps solidify the relationship between what's happening visually and what's happening within the data.
• Qualitative construction uses the feedback system in a way that can help the student manipulate relevant materials and see how the system is affected by those changes. For a virus like Zika, the variables for which operate on a continuum, this can be especially helpful for interpreting factors.

Stage model/temporal decomposition, as in ecological successions

critical event functional analysis 

aggregate behavior process analysis

• Which practices from NGSS figure prominently and less prominently in Hestenes?
• Practices 1, 2, 3 and 6 are the main tenets of Hestenes' paper. Since he calls explicitly for students to reject their preconceived (and often misconceived) notions of Newtonian mechanics in favour of recreating the world and its foundation in order to understand physics, his entire premise lies upon modeling, or practice 2. Specifically, he refers to modeling as "the explanation [of processes]" itself. He calls for students to define the Newtonian world, a direct application of practice 1. In playing the games, experimental or not, students construct and carry out investigations of the problems (practice 3), leading to the synthesis of explanations and solutions (practice 6). 
• Practices 4 (analyzing and interpreting data), 5 (using mathematics and computational thinking) 7 (engaging in argument from evidence) and 8 (obtaining, evaluating and communicating information) certainly go hand in hand with Hestenes' Newtonian world but are not the focal pieces of the paper, as they aren't as concerned about the conceptual tools of modeling that Hestenes promotes.

McMullen- VanLehn paper

From the VanLehn article, it seems as though agent-based modeling would the most valuable students to consider when modeling Zika. Agent-based modeling requires a large number of agents and predicts how the system will behave over time. Students can program the behavior of the agents and look at how the agents' behaviors affect the system as a whole. In looking at Zika, students will be able to program the behavior of the mosquitos, the people, and specific environmental conditions. This will provide them with a deeper understanding of what is critical for the spread of the virus and what impacts the virulence of it.

For students to begin this modeling exercise, the system needs to be presented to them in some way. In reading VanLehn's section on "methods for presenting systems", I view the "resources" method as most relevant. The students would be provided with a task (ex: modeling the spread of Zika) and information that is both relevant and irrelevant to the task. The information would be presented in a variety of ways (graphs, tables, papers, video, diagrams, etc.), and students would have to determine what information was necessary to include in their model and what information was not. This requires students to think critically about the system they are exploring and provides them with plenty of opportunity to revise their model during the process as information becomes more or less necessary. I also think the "virtual lab and virtual field studies" method could be helpful for students. As students simulate collecting data and exploring the environment where Zika thrives, they would be taking note of what elements they see as necessary to include in their model. The whole class could divide up different research questions and pool their information as well to increase collaboration.

When students are being introduced to modeling exercises, there need to be scaffolds in place for student success. VanLehn discusses several different types of scaffolds that could be useful for students, but the one that stood out to me as most significant was tutoring. This tutoring could take place through computer hints or through person-to-person interaction. The computer hints would be helpful for students in the moment as they are moving through the construction of their model. They would be able to get assistance on the technical aspects of model construction. The person-to-person interaction would be helpful for students in reflecting on why they are doing certain things in constructing their model, which probes for deeper understanding and encourages model revision.

Van Lehn paper

Agent based modeling through starlogonova seems to be a good way of modeling the Zika virus for students. Not only does it give students a reasonably intuitive opportunity to learn the basics of computer science, it also provides enough power for students to create simplistic models of disease transmission. The open ended nature of CS and the project-sharing capabilities of slnova also provide an environment for students to tinker and explore beyond the bounds of the lesson (e.g. "I wonder what happens if I do this?" or "How can I account for this other variable we didn't mention") both on their own time but also in collaboration with other students. While there is a reasonably steep learning curve, I suspect that students exposed to CS earlier in life may have a surprisingly easy time picking it up.

Another good option is the constraint model, which could theoretically be implemented into the agent based modeling system. Basically the constraint model takes a lot of inputs of different variables, then spits out what results could or could not happen. This could be an important way of testing how certain risk factors will affect the spread of disease. For example, students might be able to explore the effectiveness of bug nets or fogging on disease transmission, or look into how a particularly rainy rainy season might affect levels of standing water which might affect mosquito population. Creating these models would force students to think critically about the complex relationships between all the players in a given system and demonstrate how tiny changes in one area can have huge changes downstream in ways that wouldn't seem obvious at first.

I think one thing that could be a hurdle is not teaching students enough about Zika or about the mosquito vectors before setting them loose on model creation. A key part of this process that I think is somewhat glossed over is the research and legwork needed to gather the tools and build the foundation of successful models. This research could be tied in with the modeling process "What do we need to know about x to implement it into our model?" but I feel like it could be very easy to get caught up in the apparent success of modeling that we could gloss over some of the more foundational aspects of learning.

VanLehn paper- modeling techniques for Zika

All of the models discussed in the VanLehn paper would be useful for modeling zika virus. Depending on what I want the students to learn from the modeling experience changes which model would be the most beneficial. If I wanted the students to understand transmission dynamics I would have them build a model employing agent-based modeling. This would allow the students to grasp how diseases spread over time and through developing the model they would learn all the factors related to disease transmission. Developing a working model themselves would be significantly better than giving the students a model to alter. Once models were developed, each student could also present their model and students could benefit from seeing other ways to answer the same question. If I wanted students to understand cause/effect relationships, they could use the system dynamics model. The way this is explained in the text is a lot more math focused than I think would be useful for most students (especially in a middle school classroom). Diagraming the relationship between factors related to disease spread could definitely be beneficial, however, and would promote independent thought and concept mapping. I think the time spent teaching how to develop a model in this way is incredibly beneficial for students in the future and will promote students ability to connect concepts. 

Vanhenes paper

Due to the density of the paper I believe the best route for me is to talk about where students fail. In my starlogonova models one of the biggest problems I have is to get my models to predict or to reflect actual trends. I often see a disconnect for instance with the predator prey model. How does on translate for instance the predator prey relationship of sardines of South Africa’s Cape Horn as they migrate to the gathering of predators i.e.  (Black tip sharks, Common Dolphins, and flying birds) ? How can I get these models to show the Okavango wet season where Zebras and wildebeest surge through the plain and travel to the river where an armada of crocodiles feed all season? (Animal Fights Netflix).

 The implied fudge factors for this mathematical equation can vary anywhere from the ocean currents to the rains no coming at the particular time of a normal season causing a shortage in wildebeest birth. What type of algorithm can be made for this? In addition how can we translate this to a geometric or calculus side and promote inter disciplinary learning and build a better student?

Another Issue I can definitely state is the low math i.q.  I have even though I’ve passed and excelled in numerous math courses. They don’t teach this in schools enough. The common application of the math we are learning or how to translate that. How much of this is time based and how much of this is student /pto driven?  One of my biggest fears for this class is that I do the work but miss the essence of the class. In my opinion it is  as in any language to be able to recreate and reteach in an easier way then one learned. I just hope and pray that this is not my last chance to use this system and full explore it

Van Lehn article

I liked the last 3 sections before the conclusions on assessment, student difficulties, and scaffolding.  Given how important it is to consider these things ahead of time when planning model construction activities, such as the one we are doing for Zika, I appreciated Van Lehn's presentation of these.

I thought that the assessment section was very interesting.  The question of how we can assess our students' modeling skills and understanding of the scientific concepts seemed to be rather difficult prior to reading this passage.  It is good to have examples of possibilities aside from in class and written discussions/defenses of their models.  In modeling, like in everything we do as teachers, I think it is important to stress that we need to define our learning objectives first before deciding how our modeling activities and assessments of them will look, and I appreciate Van Lehn's discussion of some objectives for which modeling can be a good activity.  For the Zika modeling activity, we would need to consider how we will assess both our students' understanding of the scientific concepts we are discussing as well as their understanding and use of modeling.

I think that the misconceptions and scaffolding possibilities presented offer some great insights into our students' minds when it comes to how we can actually go about doing this.  Presumably, when we start teaching, our first classes will be our students' first classes or among their first classes to focus on modeling.  Therefore, having an idea as to the types of misconceptions they will likely bring to the activity of modeling itself on top of their misconceptions about the content will be useful.  In the same vein, the wide variety of scaffolds presented in the article will help us help our students learn how to model.  I would especially like for us to discuss meta-tutoring further in class, perhaps see it in action, because of the empirical evidence for its success.

Van Lehn Response

Van Lehn introduces many helpful models that could be used to model Zika, but what I found most interesting about his approach in teaching modeling is that he focused on scaffolding a lot. I think this is very beneficial for students that may not be familiar with how to compose models of complex systems.

As far as modeling Zika, the constraint system model would definitely work. Constraint systems predict the possible states of the behavior of a system. This model can predict how the whole system is affected if there is a change in a single variable. Zika is all about behaviors and predicting how the rest of the population is impacted If just one mosquito picks the virus up. There are several variables that could change the path of virus such as areas with a lot of standing water, or even weather, that can change the amount of mosquitoes that then might eventually be able to predict the spread of Zika.

Agent-based models, for modeling the emergent behavior of systems, would be helpful for modeling Zika too. Programming all of the variables of the system into a program such as Starlogo Nova is a useful modeling tool because students are able to physically see how the changes they input affect the whole system.

Qualitative model construction as scaffolding is a really great idea for students when first starting out with complex modeling systems. This idea suggests that students should be required to do concept mapping before doing quantitative model construction. I think this is really important. This reminded me of Hestenes, when he stressed, “one cannot discover what one cannot conceive.” The concept of really understanding the material before completing complex tasks with the material is essential to true understanding.

VanLehn: Zika Modeling

VanLehn gives a very holist view of what models can look like, how students can use them, how these models can be assessed, and what difficulties might arise in the process of creating these models. The first and most important distinction he makes is the different connotations of "modeling" in science and math versus in the science education community, "the term 'modeling' is often used more broadly to encompass any educational activity that involves a model" (373). However, for models to be truly useful, we must focus on actually creating models like we are for the Zika virus. 

One aspect of VanLehn's that I found particularly insightful was his distinction in activity types based on the type of model being made and the way the students learn about the system. This second category reminds me of Hestenes' point that often times nature may conduct experiments for us, but useful models can still be made from these examples. It seems as if a student could incorporate several presentations of systems when modeling Zika. First, while VanLehn's "resources" give the student the full information, creating the Zika virus model requires a student to know about the modes of transmission, standing water, ways to reduce risk, etc. and all of that information could be provided as a resource. The second useful strategy is virtual field studies- this is inherently what our models are all about. Furthermore, over time students could be able to test their models against real field studies as they become available and thus their models can be enhanced and corrected if necessary. 

In regards to the actual types of models students could construct, VanLehn offers three classes: constraint systems, system-dynamics, and behavior models. Here also various categories could sufficiently guide the modeling of the Zika virus. A constraint model test the outcome if one aspect of a system is disrupted. Here is where students could test the effectiveness of risk reduction methods or allow the students to isolate certain variables such as standing water, or daylight hours to see which aspects have a large role in the spread of the virus. Secondly, system-dynamics is only slightly different than a constraint model because system-dynamics can predict behavior over time- the essence of our interest into the Zika virus. VanLehn acknowledges that these models can become very complex as diagrams, which is why they can also be expressed in text. Finally, agent-based models allow the most freedom to control for the greatest number of factors and works quite easily with StarLogo Nova (our preferred modeling site).

One thing I have struggled a lot with is the issue of assessing models, which especially for a case like Zika there may not be one purely correct answer because we simply do not know the full affects yet. That is what I especially liked about VanLehn's paper- his attention to modes of assessment and what to look at. The identification of recognizing faulty behavior, troubleshooting, and extensions as the main aspects to base assessment off of really makes sense to me. Personally, I think it is the extension aspect that truly emphasizes the importance of models, through modeling students can learn about all the pieces of science and nature that interact within a system and how these systems can affect our lives.  

VanLehn Blog Post

There is little known about the Zika virus, and there are currently no sufficient models that can truly represent the transmission and spread of the virus. Students can use the information that is currently known about the virus to crest sufficient models that may be useful in fining a better understanding of the virus. "Model construction as a learning activity: a design space and review" by Kurt VanLehn offers many different modeling techniques that can be useful for students to use. The following express my thought perspective on the three models I feel would be particularly helpful in model construction of the Zika virus. 

The first modeling technique that the paper spoke about was a constraint system. VanLehn describes a constraint system as a system that predicts the possible states of the behavior of a system. It is known that mosquitoes infected with the virus are transmitting it to humans in South America. However, little is known about the how the transmission is affected based on different variables. The students can introduce different variables to influence the system, such as removal of ground water. Creating a system-dynamics model can show a  historical aspect. The paper also speaks of an agent-based model, which shows the emergent behavior of a system. This model can be the most effective in understanding the transmission of the virus. All three models can help students understand the virus, offer an explanation of the transmission, and scaffold conversations about possible solutions to control the transmission. It is important to mention that students should explore the many different factors that can be accounted when modeling the virus. 

In my opinion, VanLehn is a slight extension of the Schwarz paper, which showed how teaching students how to model scientific questions or phenomenon can be a progressive endeavor. It is important as science educators that we understand that ensuring that all students are grasping the overall concept of modeling and that we are expressing that there is no right or wrong way to create a model.  

Van Lehn Response Bottorff

When modeling Zika, students would benefit from various ideas from Van Lehn. For example, Van Lehn discusses agent-based modeling and its benefits in model construction. Besides allowing the construction of an incredibly diverse set of models, agent-based modeling allows computers to execute the model. However, students may need additional resources to learn formal language, i.e. not drawings or natural language. There are undoubtedly many misconceptions for students learning formal language that need to be addressed.

Zika is easily modeled using agents and spurs practice using formal language. Students can quickly investigate complicated manipulations of blocks even in StarLogo to model advanced aspects of Zika.

He shows that model creation is more effective than using previously made model. Students therefore are allowed sufficient time and space to think and revise. It is especially important to emphasize model creation when there is no standard model for use in new cases such as Zika. Developing the skills to make new and creative models is a necessary skill. He assesses models by both the product and the process of creation, emphasizing the importance of both.

Another benefit seen of modeling by Van Lehn is the improvement of domain knowledge alongside model creation and use. Using models can remove misconceptions, make existing knowledge more relevant and useful, and simply add new domain knowledge. Developing additional domain knowledge through and alongside the use of models can allow programmers to create models that more accurately represent nature, even Zika.

What kinds of models or activity ideas from Van Lehn do you think would be important to incorporate when having students model Zika?

Agent-based modeling described by Van Lehn would be important to incorporate when having students model Zika. It should be highly effective because creating such a model requires students to tell the computer “how to think”, indicating that they acquired a deep understanding of the process. However, it is necessary to properly scaffold the modeling activity so that students do not rely on poor problem solving strategies when constructing the model. For example, students may construct a large model before ever testing it, and then wonder why it does not work. It is therefore important to provide students with “meta-tutoring” - guidance and feedback that focus on process and not just domain knowledge (pg. 391).

In addition, students need support in learning the modeling language itself; using a completely unfamiliar language to model a phenomenon may become frustrating (it would feel similar to being asked to write a story in a foreign language). The modeling language is thus “non-trivial for students to learn” (pg. 388), and scaffolds should be in place to support students’ understanding of the modeling language.

Finally, when creating a model of Zika, students should be supported in decomposing the system into separate parts “so that one can focus on one subsystem while temporarily ignoring the rest of the system” (pg. 393). This is often a skill that students need to practice in modeling, as it is very tempting to create a complicated model without testing the validity of the individual components. 

Matt Park Hestenes and NGSS

The Hestenes article has a clear focus on modeling, which is the second NGSS practice. He goes very in depth into this practice, describing modeling as a game which students learn to play with increasing level of adeptness as they learn the subject. Hestenes also incorporates many of the other NGSS practices in varying degrees throughout his article.

Hestenes also discusses the importance of revising models as new data is obtained. This parallels the iterative nature of models as described in the NGSS practices and also reinforces the importance of experimental planning and execution. Hestenes also briefly touches on the importance of computational thinking and mathematics in model creation.

Hestenes does not cover the last couple NGSS practices. While this is not necessarily the focus of his article, I think it is an important part of students' science education to understand the personal interactions in the scientific community. The natural progression from the Hestenes article would be to have students  present their models, discuss the advantages and disadvantages of each model, and how the each others' model could be improved.

VanLehn paper

Compared to last week's article, VanLehn's paper is more technical and gets into the operational aspect of model building. The author also emphasizes on the role of model creation, rather than just working with or modifying preexisting models. The author provides examples of the sort of pitfalls that students might run into when creating models, namely, things like the tendency for students to fudge up numbers to have the model fit with the behavior of a system, rather than going back to the code to fix any structural problem within the model. The author also brings back the idea of scaffolding, which serves as a kind of extra assistance that can help students get more acquainted with modeling and more importantly, the use of programming language (like grounding and notations) to make the models. The author mentioned that there are more students who have ideas to put down in a concept map, but might not have the necessary math or computer literacy skills to put their mental models into an executable program. Thus, clearing any misconceptions about the programming language, defining terms and gents, might be the most important thing in this type of modeling. For Zika modeling, I think agent-based model would be easier compared to other types of modeling, because in essence we are tracking the behavior of free agents (i.e. mosquitoes) and changing parameters on the agent-level seems more cogent.

VanLehn Response

I think much of the work that needs to go into helping students construct a model of the spread of Zika is applicable to most, if not all, models.  A baseline of knowledge is essential to the successful construction and evaluation of models, after all.  In this vein, I think it is essential for students to be familiar and instructed in (at least briefly) the various kinds of modeling VanLehn discusses in his paper (374).  I venture that instruction in one kind of modeling (say, agent-based for Zika) could considerably streamline the classroom process, but it would do so in a self-limiting way.  If the goal of modeling is to engage students' critical thinking skills and teach them to think proactively, it should be made clear to them the flexibility modeling affords them, and the fact that modeling is as much a concept as it is a practice.  Certainly instruction may focus on a particular model for simplicity or ease of use, but students should be aware of alternatives.

In a similar vein, students should not only be aware of different types of models that exist, but also of the basic theory behind modeling; many of the problems VanLehn outlines as recurring challenges teachers face/shortcuts students take in the subject of modeling stems from a fundamental misconception of what exactly modeling is and why we do it and can lead to unmotivated student work.  Examples like inserting fudge factors, or as VanLehn put it: "models can be valid with respect to known observations but nonetheless make incorrect predictions about future observations".

Students should be instructed in the subject in question beforehand as well, in order to establish a common ground of knowledge and to familiarize students with some forms of content and the way a certain system functions.  It is unreasonable to expect, for example, a student to be able to account for the rate of the spread of Zika with respect to the resident mosquito population rates and reproduction rates; one cannot take into account factors they are not aware of or do not realize are relevant.

Practices that may be useful when specifically discussing Zika may be a focus on the decompositional model as outlined by VanLehn.  Particularly for a system with multiple variables, it may be overwhelming to students to attempt to construct a model from the start.  Instead, it may be more helpful to break down the spread of Zika into multiple components (depending on how complicated the desired model may be).  Synthesizing these components would almost certainly require considerable tweaking and bugfixing on the part of the student, but these activites in and of themselves are useful tools in understanding both how to functionally create a model and how variables interact when put together into a more complex, realistic system.  Bugfixing in itself could thus serve as a valuable introduction for students to both the scaffolding of the Zika problem and to higher order model building/functionality.

Meta-tutoring would likely be my favored method of providing feedback to the student.  Since the goal of modeling is to help students understand/think more about the natural world and how we can use these techniques to solve practical problems (or at least that is how I interpret it), meta-tutoring serves as a method of refining the student methodology and thought process.  Similarly, I think that use of natural language as outlined by VanLehn would be useful in testing how well students understand the actual workings of a system as opposed to being able to create a desired output.

John Skinner Van Lehn Response

In this article, Van Lehn outlines various practices, goals, and scaffolds that educators can use in the classroom in order to implement computational model construction. Van Lehn begins by reiterating the three different types of models that we already explored in our Zika projects--structural, functional, and behavioral. Since each of these models, as we already observed, serves to represent a different facet of model construction, it could be useful to have our students analyze the spread of Zika from each of these viewpoints before using NetLogo. (Van Lehn mentions that research has indicated that concept mapping should be done before constructing a computational model, so requiring students to think about and conceptualize the spread of Zika from a structural, functional, and behavioral lens could help them organize their thoughts and determine what aspects of Zika they may need to research prior to model construction).

Van Lehn also provides descriptions of various types of model-based thinking, which we could utilize in the classroom to help students consider the complexity and predictive power of model construction. He mentions that node diagrams and agent-based modeling can help students observe the causes and effects of manipulating single variables or behaviors, which is beneficial for overall model accuracy. Along these lines, students should develop a method for scoring their models (based on the number of accurate behaviors or conditions)--the higher the score, the more realistic the model becomes. We have discussed "curve-fitting" in modeling construction earlier in the course, and scoring a model based on certain criterion can help ensure that students' mechanisms for the spread of Zika are accurate (instead of simply matching current data). Van Lehn further posits that graphical and mathematical representations of agent-based models can allow students to make model-based predictions. Thus, before undergoing model construction, we as teachers should explicitly teach (or make sure that students already have) graphical and mathematical literacy. Lastly, the author indicates that research on modeling has shown that "meta-tutoring," with respect to modeling practices, can be beneficial for students. These tips can be divorced from direct content knowledge, as long as the students are able to comprehend and implement the various steps of effective model-making.

While these all seem to be good points, I did leave the article with a few unanswered questions. With regards to meta-tutoring, the author observed students using Betty's Brain program, which contained built-in meta-modeling construction. Assuming our students will be using NetLogo, which does not contain a built-in meta-tutoring component, what can we do to ensure that students think about the practices inherent in model construction and deployment? Also, as per usual, I wonder about the time commitments required in classrooms to effectively implement computational modeling. Van Lehn elaborated on the duration of NetLogo training in students who built models (48 hours) and students who simply explored existing models (2 hours). Given this much larger training period for students to create their own models, when is it appropriate to have students explore pre-made models, and when should they be required to build their own?

Hestenes: Modeling Games 

While Hestenes' article may not technically be new, I find that it has a refreshing view on modeling as an aspect of Science Education that has not formally been introduced until the new NGSS was published. If one is to only take one aspect away from this article it should be the notion of modeling as the "great game of science." Science beyond the scope of science taught in a classroom, both old and new discoveries all depend on models to become valid and useful. This notion is not one that is explicitly shared outside of the scientific community, at least not in my experience. How could introducing the topic of science as an concrete yet ever-evolving body of knowledge change the way science education is taught? If we take the idea of science and modeling as a game and use that same framework in science education the NGSS standards can be easily incorporated. 

There are several key examples of how Hestenes' principles of modeling fit the NGSS standards, beyond the obvious connection to Practice 2. Developing and using models. One practice I saw a lot of reference to was 3. Planning and Carrying Out Investigations. One idea Hestenes presents is that "conceptual invention and empirical discovery go hand-in-hand," which can lead to the importance of experiments and investigations to understand or discover scientific principles. While, experiments are not models, if students are able to construct models from their own data there is a better chance that the conceptual importance of an experiment will truly hit home. This not only incorporates modeling into curriculum but also necessitates that experiments be made to challenge students with more thought-provoking questions. On contingency to this use of models to demonstrate Practice 3 was the idea that in some cases Nature does the experiment thus data collection and interpretation is the main task required of students. 

In addition to the connection to Practice 3, I believe Hestenes allusion to the use of computers/technology allow for the incorporation of Practices 4,6, and 8. Computer programs like NetLogo and Star Logo Nova both allow for complec systems to be modeled in a way that students could not physically create or observe given the time, money, and logistical constraints. Some models are able to produce graphs of data over time or space and thus students could be able to analyze and interpret that data based on their model, practice 4. I can see this being incorporated into deployment games because the model can account for new data and see if that data also fits the model. This can showcase Practice 6: Constructing explanations, if a teacher frames the game in this way. If the data cannot be included students can create a plausible explanation as to why via the model they made, or conversely why certain data is allowed to be included. One of the best ways to implement analyzing data and constructing explanations is to allow students to have an integral part of the process. 

Hestenes focused intensely on physics and how discoveries in physics were rooted in models before his applications to science education; yet, it is the applications of models in science education which I believe can be the most important. The NGSS Practices 7 and 8, engaging in argumentation from evidence and obtaining, evaluating, and communicating information, respectively offer the best connection between science education and the world of science. Through models students can be shown how to take the data the create and answer the question- so what? Knowing the big picture applications of the foundational science learned in schools can help students move beyond the rote memorization of facts, theories, and laws and to the more analytical side of science. 

NGSS Practices in Hestenes

All of the NGSS practices are discussed and suggested in Hestenes; however, some are done more so than others.  I will go through the practices and briefly discuss their role in Hestenes's conception of science instruction through modeling.

1. Asking questions & defining problems - Hestenes frames this in the sense that science instruction should help students develop conceptual explanations for real world problems.  He says answers to the questions and/or solutions to the problems can be determined through theoretical and experimental modeling games.
2. Developing & using models - developing models is the point of the article.  He also discusses how to use/validate models by utilizing their predictive power.  However, one of the primary points of the article is to explicitly teach modeling - what it is and how to do it - and then applying it to our specific content areas.
3. Planning & carrying out investigations - this is not emphasized as much beyond using the model to predict a testable outcome and carrying out investigations to determine how accurate or valid the model is
4. Analyzing and interpreting data - Hestenes spends a small amount of time discussing the importance of this, but it is couched in the broader framework of determining a model's validity and understanding the story.
5. Using mathematics and computational thinking - similar to 4, however there is also emphasis on the importance of pattern recognition in creating models, a skill also important in computational thinking.
6. Constructing explanations & defining solutions - Like 2, this is one of the points of the article.  However, revision of the model, something discussed at length in other articles we've read, is not strongly emphasized in Hestenes's article.  However, the way in which students construct explanations, backed by psychology, is emphasized in the discussion.  I especially liked the emphasis on reflective thinking to help students' metacognitive skills.
7. Engaging in argument from evidence - this is not as emphasized, but it is a necessary skill in defending the model that students create and forms a foundation for some of the concepts Hestenes discussed.
8. Obtaining, communicating, and evaluating information - this is probably the most underemphasized in the article; however, Hestenes describes the process of obtaining and communicating information and discusses in the conclusion the importance of evaluating the information.  He recommends a modified Socratic method to most effectively obtain and evaluate the new information while still retaining a direction and educational objective.

McMullen- Hestenes and NGSS

Before exploring the NGSS practices featured in Hestenes paper, "Modeling Games in the Newtonian World", it is useful to review what the practices are. The seven NGSS practices are:

"1. Asking questions (for science) and defining problems (for engineering) 
2. Developing and using models

3. Planning and carrying out investigations
4. Analyzing and interpreting data 
5. Using mathematics and computational thinking
6. Constructing explanations (for science) and designing solutions (for engineering) 
7. Engaging in argument from evidence
8. Obtaining, evaluating, and communicating information" (NGSS Framework p. 42).

For starters, Hestenes paper is focused on modeling (theoretical and experimental games to model the Newtonian world), so the second NGSS practice is covered very thoroughly. From start to finish, the paper always come back to modeling with Hestenes even saying that "the main objective of science instruction should therefore be to teach the modeling game" (Hestenes 732). Hestenes talks about constructing explanations (NGSS practice 6) as they pertain to modeling. He states that the model is the explanation. If a phenomena can be modeled within a theory, then the theory explains the model. However, he says we don't often use modeling as explanation which leaves our explanations as merely partial or qualitative explanations. With respect to NGSS practices 3, 4, and 5, Hestenes addresses them at various points in his discussion of experimental games. 

While Hestenes implemented a good number of the NGSS practices in his paper, I wished he would have addressed practices 1, 7, and 8 more thoroughly. There was little discussion about evaluating and communicating information or engaging in argument from evidence. There was a brief mention of asking questions, but I would have appreciated a discussion on asking questions with respect to phenomena and theories that are already well understood. 

Which NGSS practices figure prominently and less prominently in Hestenes?

Hestenes uses constructivist theory as a framework for science education. Constructivist theory “implies that understanding is a creative act,” and that scientific theories cannot be “transmitted like a TV image; it must be created anew in the student’s mind, and only the student can do it” (pg. 745). This idea is also a theme in NGSS; it emphasizes the importance of engaging students in the practice of science (for example, through modeling, argumentation and analysis). Thus, NGSS not only specifies what students should know, but also specifies what students should be able to create.

Both NGSS and Hestenes focus on the central importance of modeling in knowledge construction. According to Hestenes,“Students should be taught from the beginning that the game of physics is to develop and validate models of physical phenomena” (pg 747). In addition, both NGSS and Hestenes express the notion that the scientific method is only one of part of the overall modeling process, and that it should be taught as such. Students should design and interpret their own experiments with the understanding that “models play an essential formal role in both activities.” Finally, NGSS and Hestenes recognize that model building is necessary but not sufficient – the addition of “post-mortem analysis” is also necessary for growth to take place. Thus, argumentation and critical analysis should be part of the modeling process in science education.

Hestenes argues that learning science is like learning how to play a game – not only do students need to know the rules in order to play, but they also need to know how to play well before they can ever change the rules. Even if they never reach the point of being able to change the rules (only a few reach this level) it is still very possible to teach students how to “play a credible game and appreciate the achievements of the grandmasters” (Pg. 747).

Hestenes paper and NGSS

I liked the Hestenes paper because it presents physics as a continual refinement of models to explain physical phenomena. It is really different from the way I was taught physics, which was mostly memorizing equations and learning which equations would fit which situations on test problems. The Newtonian World game is really the way physics should be taught because it goes through the procedural thinking and reasoning behind coming up with the models, and it seems to be essential in dispelling preconceived misconceptions that are hard to wipe away even after studying a year of college physics. It fits all eight practices. The most important ones include 1) asking questions, 2) developing and using models, 5) using mathematics and computational thinking, and 6) constructing explanations. I also believe that students playing the "game" would also need to have a lot of prior knowledge, especially in mathematics and/or proficiency in using modeling software, to construct models on their own to explain measurements of the physical world. To Hestenes it seems very straightforward that adopting the game is a surefire way to ensure student understanding of physics, but questions remain in how much assistance or hand-holding students require to go through the game, because it is certainly easier and more straight-forward to teach the traditional way, regardless eventual student understanding.

NGSS and the Hestenes Paper

"Modeling games in the Newtonian World" has many prominent parallels to the standards presented in the NGSS. When reading the paper, I utilized the eight practices given in Chapter 3 of the NGSS to identify them in the Hestenes paper. It was interesting to find so many of the same ideas in both readings.

Hestenes spoke about how "one cannot discover what one cannot conceive". This is important for students because without having an ability to conceive a concept, they cannot understand that they know it. The practices are very useful in this because they allow for this to  occur. The paper is filled with many different examples of the practices (both obvious and embedded) listed in the NGSS. For example, the mention of theoretical games, such as model building, ramification, and deployment have different components of the practices, such as asking questions and planning and carrying out investigations. The paper also gives various ways to utilize the practices, which allows flexibility. This is a good trait to have because it is important that students know that there is no right or wrong answer to utilizing the practices in scientific discovery. Another interesting parallel I found was that of Figure 3-1 in NGSS and Figure 5 of Hestenes. Both speak of having components that must balance out with elements of understanding, investigation/interpretation, and creation. Overall, I will admit that with my minute prior knowledge of physics, the paper was easier to follow than I expected.

Hestenes and NGSS

While reading Hestenes, I had each of the NGSS practices up to refer back to throughout the article. The first NGSS practice, asking questions and defining problems, seemed to be important to Hestenes. He kept making a point that I definitely agree with,  “one cannot discover what one cannot conceive.” He goes on to say kids believe their job is to memorize facts and that this explains why so many students have a tough time grasping modeling. I believe this fits in with the first practice because in order for students to learn, they need to be asking questions. Hestenes does make a point to say that many students have misconceptions bout modeling, and I believe asking questions is the best way to break down the learning barriers. The second practice, developing and using models, was essentially the whole point of Hestenes’ article, as he believes the main objective of science instruction should be to teach modeling. Even one of the several general principles of experimental design is developing a model for phenomena from theory, which goes to show the importance of this practice.

Planning and carrying out investigations was touched upon in his explanation of experimental games. The objective of these games is to test and validate models. The experimental games have two major components: design and interpretation of data and the collection and interpretation of data, which leads to the fourth NGSS practice, analyzing and interpreting data. Hestenes talks about this practice when he refers to Zeroth law and other physical laws that are involved in increasing range and precision of kinetical measurements.  He explains how data are meaningful only to the extent that they are related to some conceptual model, and that the data should relate to some model or process to be useful. Using mathematics and computational thinking was touched on briefly. With the discussion of Newton's discovery of the binomial theorem as good index of his growing analytical and pattern recognition skills, and explaining that Newton was the first to apply calculus to practical problems, there was an evident importance of the meshing of mathematics and science.

The sixth NGSS practice, constructing explanations and designing solutions, was brought up when talking about how student attention should be directed at models and processes where patterns are found. I interpret this as actively construction explanation is essential to the model process and requires the utmost control of student attention. For engaging in argument from evidence
, Hestenes talked about how reflective thinking is essential for mastering technical skills. He made a point to say how practice is not enough. Lastly, for obtaining, evaluating, and communicating information, Hestenes talked about deployment games, which involve matching of models to empirical phenomena and data. Often a given model has already has already been validated in some empirical domain so the model is expected to account for new data. Using obtained information, evaluating it by seeing what can be added to add validation and communicating new findings can ultimately lead to new knowledge.

Hestenes & NGSS Bottorff

Hestenes focuses mostly on modeling and therefore clearly covers the NGSS practice of developing and using models. Specifically, Hestenes details various games that can be used to develop and use models. His theoretical games work to build models from data (building), study the properties of models (ramification), or match models to new data (deployment). He asserts that deployment is an empirical component of modeling, and this statement pairs nicely with NGSS practices. Matching models to new data, i.e. revising models, is a central component of modeling. He also details experimental games whose focus is to test and validate models, also fitting nicely in with NGSS model revision.

According to Hestenes, the ultimate game is to discover the rules of the game of nature (i.e how the universe works). Thus, there's a strong connection to other NGSS practices: asking questions (for science) and planning and carrying out investigations. Indeed, one much be able to ask questions and investigate phenomena in order to detail the rules of the universe!

Despite a strong link between Hestenes and various NGSS practices, there is room for improvement. Although Hestenes does mention the use of data analysis and mathematics to create models and game rules, there is room for development using computational thinking! However, one must consider that Hestenes proposed these ideas two decades ago when computational thinking was harder to implement in the classroom; NGSS, on the other hand, proposes practices understanding modern computational power. Additionally, I think there is room for engineering and physics-centered games to develop in students' minds together. Clearly, physics and engineering and closely linked, and defining problems and designing solutions could hold a much larger part in the use and development of models in the context of games.

Hestenes Vs NGSS

In my reading of  Hestenes I see quality research done.  He begins is argument in a strong way stating that modeling is the name of the game and an base layer for science and technology. This I can definitely agree with.  The first and easiest way we remembered  science was through a lab. The labs are examples of conceptual models. They take our  learned knowledge and reinforce them with application. Therefore creating a sort of scientific knowledge. He then goes onto  elaborate on the importance of intrinsic vs. interactive values in the models. These both play  significant roles; intrinsic values are set values or constants while interactive values allow us to see the ebbs and flows of the model and allow it to  grow and change. Hestenes then began to talk about the laws of change and natural laws which further solidified his basis for the intrinsic vs. interactive models. This brings to mind my genetics class where we saw how bacteria live and in full effect were cultured in an agar and left to incubate. The natural law told us that these bacteria would grow and could be seen in front of our eyes. While the law of change showed us that the bacteria would become different shapes, colors, have a wide range of dispersion or small depending on how they were handled.
The next 3 sections spoke on how to incorporate these models with your students. While NGSS statutes have taken a while to hold on. Improved test scores (as we all know data driven schools seem to succeed more) have proven that these two schools of thought can be mixed and are needed for successful thinking and teaching.

Hestenes Reading Response Thomas Plaxco

 I found Hestenes to be in line in many ways with the NGSS standards, if perhaps a bit prototypical.  Of course, this is fair given the fact that Hestenes wrote this article over 20 years prior.  Naturally, it embodies similar themes to the NGSS standards, but it places emphasis on different areas and focuses on others less.

At its core, for Hestenes, the education of science is fundamentally that of models.  While Hestenes goes to lengths to qualify this and stress that access to scientific information is still an essential aspect of one's scientific learning, the focus of Hestenes's ideal is undoubtedly that of modeling.  For Hestenes, modeling represents the only real way for students to learn science, as the practice of science as the author argues is simply the creation and refinement of models, albeit more complicated ones.

Certainly this is in line with NGSS standards, particularly with the focus on model-making and the collection/interpretation of data.  However, NGSS standards and Hestenes's theory do differ in some small, yet important ways.  These deal primarily with the issue of what is focused on in the classroom.

For example, Hestenes discussed at length the importance of providing context to historical scientific breakthroughs, as illustrated by modeling.  While one could certainly argue this is a somewhat implicit goal of NGSS standards, it is something not stated as explicitly as Hestenes.  By contrast, the NGSS standards of using mathematics and developing argumentative skills are certainly ideas that would be valued by Hestenes, but they are not necessarily explicit goals.  Rather, I feel that Hestenes would argue that mathematical literacy is somewhat of an implied tool to be used in model formulation and data evaluation, as is the role in argumentation and communication in refining one's models.

That being said, I believe there is an important distinction between what is implied and what is stated.  For Hestenes, while I feel they certainly share similar goals in forming/nurturing scientific literacy, the focus or implementation seems to have slightly different goals.