Monthly Archives: March 2017

In this week’s readings, I was drawn to the connection between learning and physical activities, actions, and gestures. As Winn (2003) points out, “successful students are anything but passive” (p. 13) and our classrooms must reflect and foster this fact. If students are to involve their entire bodies in learning, rather than just their brains, thereby embodying cognition as Winn describes it, then as educators, we must look carefully at how we construct our lessons and projects to support this. As Winn discusses, our natural views of the world are very limited and are based on our own experiences. While some digital technologies may recreate experiences for us, the question becomes how accurately can a computer programmer recreate a unique experience that each individual user’s knowledge and understanding can evolve from?

Ahmed and Parsons (2013) offer that mobile technologies provide students with “opportunities for increasing engagement, motivation and learning (Lin, Fulford, Ho, Iyoda, & Ackerman, 2012)” (p. 62). Their study uses a mobile learning application called “ThinknLearn” to engage students in abductive scientific inquiry while scaffolding learning so students are able to generate hypotheses based on inferences made through observations. The study places students in a real-life environment where they follow the Abductive Inquiry Model (Oh, 2011, as cited by Ahmed and Parsons, 2013, p. 64) of “exploration, examination, selection, and explanation” (p. 64) to collect and examine data with the aid of the “ThinknLearn” application to enhance learning experiences, performance, and critical thinking skills.

In a “no-tech” embodiment of learning, Novack, Congdon, Hemani-Lopez, and Goldin-Meadow (2014) explore the effect of physical action, concrete gesture, and abstract gesture in helping grade three students solve math equivalence problems, and beyond that, students’ abilities to generalize what they learned to a new concept. While the study showed that the students “were equally likely to succeed on the trained problems after instruction” (p. 5), researchers found “acting gave children a relatively shallow understanding of a novel math concept, whereas gesturing led to deeper and more flexible learning” (p. 6) with abstract gesture aiding generalization and concrete gesture leading to conceptual understanding.

In my own practice, I can see embodied learning being used to support a simple machines unit in science. Students can plan their machines using gestures and actions (in groups – incorporates social learning/collaboration), and will then build and test their machines in a trial-and-error learning environment, basing their learning on their interactions with the machines they are themselves creating. Technology can be brought in as students research and watch videos to help them overcome difficulties they face throughout the duration of their projects.

Questions for discussion:

1) Is it possible for a student to interact authentically in an artificial environment, given that the environment is created using if-then models based on predictable outcomes?

2) How does the increased integration of digital technology into the classroom impact the physical activities that connect students to learning? Do activities or actions performed using an electronic device connect students to learning in a similar way that physical activities or actions would in a traditional classroom setting?

3) “An artificial environment is completely predictable, because we have made it” (Winn, 2003, p. 13), but how does the environment that corresponds with the programmers’ views of the world match up with the experiences and understandings of an individual user? How does an artificial environment impact the learning of students who are from a different cultural background than the programmer? For example, how would an Indigenous student’s own experiences and knowledges be represented in an artificial environment created by a Western European? How would the experiences of a student who has recently arrived as a refuge be represented in an artificial environment created in North America?

References:

Ahmed, S., & Parsons, D. (2013). Abductive science inquiry using mobile devices in the classroom. Computers & Education, 63, 62-72. Doi: 10.1016/j.compedu.2012.11.017

Novack, M. A., Congdon, E. L., Hemani-Lopez, N., & Goldin-Meadow, S. (2014). From action to abstraction: Using the hands to learn math. Psychological Science, 25(4), 903-910. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3984351/

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 1-28. Retrieved from: http://isites.harvard.edu/fs/docs/icb.topic1028641.files/Winn2003.pdf

Lindgren and Johnson-Glenberg (2013) discuss the implications of combining embodied learning and advancing technologies. In short, scientific and mathematical concepts are learned through physical, natural movement (such as “gestures, touch, body position”). This learning is combined with new technologies in an emerging field of education known as mixed reality (MR). The authors suggest six precepts for consideration in embodied learning and mixed reality classrooms. They are briefly summarized below:

1. Embodied learning benefits everyone and not just a subset of the population
2. The physical aspects must be properly linked to the development of new ideas
3. The environment should not just augment reality (for example, it should accommodate the ability to overlay visuals or audio or induce collaboration)
4. Provide opportunities for student and peer collaboration
5. Where possible, combine both theory-driven studies and controlled studies to inform the MR classroom
6. 6) Revise assessment to address the changing learning environment

Coella (2009) examines the use of participatory simulations in which a variety of scenarios are guided by a series of rules and structure. Within these constraints, students are able to learn scientific concepts through inquiry, experimentation, and exploration. However, these computer-supported simulations are not actually conducted on the computer; instead, students participate by wearing small, wearable computers and are participants in “unique, life-sized games.” For example, the interactions within a pond ecology were studied by students interacting with each other as either a “big fish” or a “small fish.” Results from the study indicated students were able to: be engaged, identify problems and produce hypotheses, and design and execute relevant experiments.

It is evident from both studies (Lindgren and Johnson-Glenberg, 2013; Coella, 2009) that learning occurs through experience. Students need to be afforded the opportunity to learn through of a diverse array of experiences, whether it be a regulated real-life simulation using hand-held devices or through the physical movement of the body. The concept of kinematics is able to utilize the concepts proposed by embodied learning and mixed reality. For instance, students should be able to physically measure their movements to produce corresponding kinematics graphs. Undoubtedly, the study of motion should inherently involve movement and not rely on textbook recitation or didactic methods, such as lecture.

Questions:

1. The article by Lindgren and Johnson-Glenberg (2013) discuss that assessment needs to depart from “traditional paper-and-pencil-style assessments” and parallel constructivist-inspired learning. How would possibly alter your assessment to match non-traditional learning environments, like MR?
2. In my experience, while educators would like to incorporate different strategies in their lessons (like MR or participatory simulations). They can be sometimes difficult to execute effectively because programs or applications were not necessarily developed with a pedagogical mindset to begin with. Have you encountered any such challenges and how did you overcome them?

References

Colella, V. (2000). Participatory simulations: Building collaborative understanding through immersive dynamic modeling. The Journal of the Learning Sciences, 9(4), 471-500

Lindgren, R., & Johnson-Glenberg, M. (2013). Emboldened by embodiment: Six precepts for research on embodied learning and mixed reality. Educational Researcher, 42(8), 445-452.

If the Wicked Witch of the West co-authored this week’s reading, it may have been subtitled,

“U’m- Welting!”

I always know when I am enjoying a week more than others, based on the amount of effort I put into the reading and note taking.  And without any sarcastic undertones, I can honestly say that this week was a huge time suck. Perhaps it is the “science-geek” in me that really favors learning about theories that are neurologically situated (I’m not a neurologist, and I don’t even play one on TV, but neurological research unquestionably fascinates me). Perhaps it is that I am a self-proclaimed Queen of Analogies.  All I know is that this week really blew my hair back!  Floated my boat! I really picked up what the authors were laying down!  Hopefully, you are reading my mail, here.  (OK… I think I’m done now.)

If you did not read, “Understanding Needs Embodiment: A Theory-Guided Reanalysis of the Role of Metaphors and Analogies in Understanding Science” (Neibert, Marsch, & Treagust, 2012), I highly recommend that you save the PDF for recreational reading at a later time.  Although you may not profess to be the King, Queen, and/or Joker of Analogies in your classroom, there is no possible way that one can avoid using analogies/metaphors (and yes, there is a difference) within one’s day-to-day speech.  The authors provide a simple example such as “I see your point” as a metaphorical representation of understanding and vision.  As a teacher in a school with 20% of the population being in our International Program, I am very careful to explain some of our “weird” Canadian sayings— just this week I was explaining the analogy “Six of one and half dozen of the other.”

So what makes a great analogy/metaphor (a/m) versus an ineffective analogy/metaphor?

• Your a/m should utilize everyday embodied sources that ALSO can be imaginable—it is ineffective to use sources for our a/m that a student hasn’t any personal experience with and/or can not relate to.
• The learning goal (target domain) should involve a first or second-hand direct learning experience—have students actually touch things!
• Models can serve as an embodied source domain that enables reexperience and reflection opportunities surrounding abstract concepts.
• Recognize the limitations of the a/m: they may bring to light (“highlight”) the key ideas yet simultaneously misinform (“hide”) other related concepts.

Questions to chew on:

1. What is your favorite analogy or metaphor to use in a science or math context? How do you know that it is an effective analogy? (It’s OK if you don’t!)
2. Have you ever had an analogy or metaphor “backfire” on you?

Looks like I’m past my word count… time to make like a baby and head out!

P.S. In case you were curious…

References

Niebert, K., Marsch, S., & Treagust, D. F. (2012). Understanding needs embodiment: A theory‐guided reanalysis of the role of metaphors and analogies in understanding science. Science Education, 96(5), 849-877. http://ezproxy.library.ubc.ca/login?url=http://dx.doi.org/ 10.1002/sce.21026

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114. Full-text document retrieved on January 17, 2013, from: http://www.hitl.washington.edu/people/tfurness/courses/inde543/READINGS-03/WINN/winnpaper2.pdf

Attempts to understand the psychology of learning has led to a variety of perspectives of cognition. While learning and activities have been common place in classrooms, Winn (2003) suggests that cognition is deeply tied to learning and the activities used for learning. Traditionally, the approach to cognition and adaptation of technology use, namely that it had to do with connecting knowledge with its representation as symbols in the mind. However, this approach removed the environmental context from the individual’s unique process of understanding. Instead, cognition is, Winn argues, embodied in physical activities, with the activities embedded in the environment. Learning then is a result of the connection of the learner between their cognition and the environment via their external body, the process which Winn terms embodiment.

This concept is support by Novack, Congdon, Hemani-Lopez, & Goldin-Meadow (2014) who explored embodied learning with third graders learning math by separating a group of students and providing each group a different learning method: one with physical actions with objects, one with concrete gestures, and one with abstract gestures. While all three groups learned to solve the problems they were presented with, they found that acting with objects only provided students with a shallow understanding of the math concept, quantified through pre- and post-testing of student knowledge. By contrast, the abstract gestures allowed students to develop a more generalised understanding that allowed them to solve more complex problems as well. This supports the Winn’s notion that learning cannot simply be a structured representation of one approach, it must be contextually relevant to the student’s environment and, more importantly, be relevant to their individual perception of said environment.

Both Winn and Novack et al. support the notion that an individualised learning experience is more effective and leads to a more generalised and better understanding, and embodied learning is more able to cater to this type of learning. Thus, technology use in the classroom should focus no only on connecting ideas to symbols, but to enhance the embodiment of learning. Bujak, Radu, Catrambone, MacIntyre, Zheng, and Golubski (2013) extends this further by suggesting augmented reality (AR) combines the strengths of virtual learning environments with the context of reality. Compared to virtual reality (VR) which seeks to replace the real environment, AR adds to the real environment which allows “the creation of embodied metaphors inspired by physical manipulatives, or new kinds of metaphors otherwise difficult to convey through concrete physical objects.”

In my STEM classrooms, this does serve to add an extra factor to consider when designing lessons and units. Activities that may seem to be open and allow for constructivist learning may not accomplish that task if the connections that students make are not unique to themselves. Instead, activities need to balance focus on a specific topic while still allowing the freedom for students to engage with the activities and embody their learning.

Some questions for consideration:
1. Winn notes that a virtual reality learning environment is inherently limited because the interactions and responses between user and environment are pre-programmed, and thus not unique to the user. If virtual reality, as Bujak et al. argues, cannot accurately simulate the tactility of real-life, do VR and simulations still have a place in learning? How worthwhile would any learning be?

2. Science in elementary and high school focuses primarily on “playing catch up” with the vast amount of scientific knowledge currently available, so that students can eventually move to the forefront and discover new scientific knowledge. If that statement is true and science learning leading up to that point is about competence in scientific facts, then how does embodied learning fit into that goal? Does specifying a specific, focused assessment of a lab experiment rob not students the opportunity to learn within their own context? Should there be concern with students constructing their own knowledge that is deeper and more personal, but counter to commonly accepted scientific understanding?

In addition to Winn (2003), I have deliberately selected two articles that provide insight on my TELE assignment and future teaching environment (as I plan to implement my TELE project for my students in September). The second article was on the integration of handheld technologies in a WISE project (Aleahmad & Slotta, 2002). The third article surrounds student conceptions of global warming (Niebert & Gropengießer, 2013). Both of these articles are complementary to my purpose because for my TELE I am interested in redesigning a current WISE project on global warming and cater it to my grade 7 students in September. From the three articles it has demonstrated that learning occurs when there is interactions between internal conceptions (e.g. cognitive), external activities (e.g. scaffolding) and environmental influences (e.g. handheld devices, experiments, etc.). Learning is complex and requires what I informally call, kinesthetic learning where students need to be physically active participants in the learning process in an embodied and embedded way that requires them to adapt and modify their conceptions. In Niebert & Gropengießer (2013), researchers analyzed scientists and students’ conceptions of climate change using the Model of Educational Reconstruction (MER) approach where they used misconceptions as starting points to recreate learning activities to target them.I found this perspective implicated a teaching strategy where I could use a version of a “Knowledge-Wonder-Learn” activity to assess my students’ prior knowledge about climate change. Misconceptions would appear here and I could utilize them to cater lessons to address them. The article also emphasized the challenge for students to grasp a concept like the greenhouse effect because it is not easily visualized by students microscopically (e.g. global warming as progressed through hundreds of years_ and therefore, it makes it difficult for them to understand it. However, through hands-on experiments and activities, students can visualize the issue of climate change visible and operationalized so that they can then reflect on their misconceptions about this topic. In the third article by Aleahmad and Slotta (2002), it showed how to integrate handheld technologies into an already technology enhanced learning environment such as WISE where it expanded the opportunities for collaboration and scaffolding. Students would use handheld devices, which I assume could be iPads and tablets these days to obtain data from the outside world (e.g. surveys, field observations) and enter them into the same database so that the entire class can use the data for further learning. With the topic of climate change, using handheld technologies students can conduct interviews with scientists, take pictures of the environment (e.g. evidence of global warming), and collect field data (e.g. sea level, water quality) and pool them together with other students. This makes the learning authentic because students can explore and share different information. Since WISE is typically a partner project, integrating handheld technologies will allow groups to collaborate with one another to provide further scaffolding opportunities.

Questions for discussion:

1. What are some potential constraints of Winn’s (2003) proposal of a learning environment that consists of embodiment, embeddedness and dynamic adaptation?
2. Are there other suggestions you can provide about integrating handheld technology into a topic related to climate change?
3. Is it possible that some learning activities (e.g. experiments and other hands-on opportunities) are not effective at challenging students’ misconceptions about a topic and if so, what can an educator do?

Aleahmad, T. & Slotta, J. (2002). Integrating handheld Technology and web-based science activities: New educational opportunities. Paper presented at ED-MEDIA 2002 World Conference on Educational Multimedia, Hypermedia & Telecommunications. Proceedings (14th, Denver, Colorado, June 24-29, 2002); see IR 021 687.

Niebert, K., & Gropengießer, H. (2013). Understanding the Greenhouse Effect by embodiment–analysing and using students’ and scientists’ conceptual resources. International Journal of Science Education, 1-27.

Winn, W. (2003). Learning in artificial environments: Embodiment, embeddedness, and dynamic adaptation. Technology, Instruction, Cognition and Learning, 1(1), 87-114.