Monthly Archives: August 2017

Periodicity Wikispaces

Here’s a link to my final project, creating three artifacts to teach Periodicity in Chemistry:

http://periodicity.wikispaces.com/

Password: etecstudent

T-GEM, PhET, and Water Conductivity

Many concepts are difficult for students to understand without some form of visualization to aid the description. This week I have integrated T-GEM with a water conductivity PhET Simulation to create a lesson activity which can address misconceptions. A regular misconception that students have is that pure water conducts electricity. By the end of this lesson students should be able to communicate what makes water conductive.

Step 1: Introduction

-Students form groups of 2-3, each group will have a computer, paper, and writing utensil

-Review as a class:

1 – What is electricity? What is flowing?

2 – How do we make electricity?

3 – What is concentration (with respect to dissolved solids)?

4 – How could we make things more or less concentrated?

Step 2: Generate

Have students as a group generate ideas around the following key questions:

1 – Is water conductive?

2 – How does electricity move in water?

3 – Would dissolving solids in the water change its properties?

Each group will write their predictions down on a piece of paper.

Step 3: Evaluate

Have groups use PhET Simulation “Sugar and Solutions” to investigate water conductivity.

Ask students to test if their predictions are correct.

Step 4: Modify

Ask students to create a situation where their new knowledge of water conductivity could be useful.

(The use of Makey-Makeys can be used so students can make an apparatus which physically uses water conductivity to control a computer)

Step 5: Reflect

Have students revise their original ideas and together formulate the main points of water conductivity.

References

Friedrichsen, P. M., & Pallant, A. (2007). French fries, dialysis tubing & computer models: Teaching diffusion & osmosis through inquiry & modeling. The American Biology Teacher, 69(2), 22-27.

Khan, S. (2010). New Pedagogies on Teaching Science with Computer Simulations. J Sci Educ Technol, 20(3), 215–232. http://doi.org/10.1007/s10956-010-9247-2

Srinivasan, S., Perez, L. C., Palmer,R., Brooks,D., Wilson,K., & Fowler. D. (2006). Reality versus simulation. Journal of Science Education and Technology, 15 (2), 137-141. http://ezproxy.library.ubc.ca/login?url=http://dx.doi.org/10.1007/s10956-006-9007-5

E-FOLIO

https://blogs.ubc.ca/533james/

Knowledge construction in the real world

Traditionally, educational professionals believed that knowledge in math or science must be constructed by first learning the simple mechanical and fact-based aspects before being able to integrate these fundamentals into real-world problems. While it may make sense to construct a building by first focusing on fundamental pieces such as a foundation and framing, this method may be too simplified to apply to students who are embodied in a world with a plethora of problems to be solved, some of which they may never have experienced before. Carraher et al (1985) looked at math skills in the practical world and discovered that youth with very little formal education developed successful strategies to deal with real life mathematical problems in a market. The youth could successfully solve 95% of problems in the informal market setting while only being able to successfully solve 73% of the problems given to them in a formal test setting. “It seems quite possible that children might have difficulty with routines learned at school and yet at the same time be able to solve the mathematical problems for which these routines were devised in other more effective ways” (Carraher et al, 1985). Thus, as educators it can be useful to use real-life problems in the world to help students gain more applicable and effective knowledge.

Two ways in which students can use real-life experiences to guide their learning is through networked communities such as GLOBE and Exploratorium. In the GLOBE project, scientists are linked with teachers and students to gather data from around the world (Butler & MacGregor, 2003). Students are taught data collection techniques and can visually display their and other’s collected data to analyse and interpret. An example of such is looking at the carbon cycle in different biomes; students collect topsoil data from their region and compare it with data from other students in different parts of the world. With this program, students can directly participate in global knowledge generation on a global scale. Further, Exploratorium presents a virtual museum which allows students to interact and learn with interactive tools, hands-on activities, apps, blogs, and videos to learn about science. “Many innovative educational applications, tools, and experiences are being specifically designed to capture the interests and attention of learners to support everyday learning” (Hsi, 2008). Such tools allow students to generate knowledge in and out of the classroom as the line between formal and informal education becomes blurred. The goal from informal learning is to create a passion for life-long learning in students. If students can self-motivate, knowledge construction can become limitless.

Butler, D.M., & MacGregor, I.D. (2003). GLOBE: Science and education. Journal of Geoscience Education, 51(1), 9-20.

Carraher, T. N., Carraher, D. W., & Schliemann, A. D. (1985). Mathematics in the streets and in schools. British journal of developmental psychology, 3(1), 21-29.

Hsi, S. (2008). Information technologies for informal learning in museums and out-of-school settings. International handbook of information technology in primary and secondary education, 20(9), 891-899.

Embodied learning and gestures

Embodied learning revolves around the notion that it is not just the brain participating in learning activities, rather the whole body participates, interacts, and manufactures new concepts (Winn, 2003). Winn works to defeat the notion that the mind and body are separate entities, rather the interactions we have with our environment is key to the learning process. Removal of environmental learning experiences works to limit student’s ability to adapt with the environment and therefore form a more complete learning experience. Winn (2003) encourages educators to create a “framework that integrates three concepts, embodiment, embeddedness and adaptation.” This embodied learning can be extended to artificial environments where students actively engage and become “coupled” (Winn, 2003). A person in a coupled learning environment actively engages and interacts through problem solving and discovery learning.

The use of mobile technology can allow students to become coupled learners with artificial environments. “They also have the potential to establish participatory narratives that can aid learners in developing a contextual understanding of what are all too often presented as decontextualized scientific facts, concepts, or principles” (Barab & Dede, 2007). Mobile technologies can allow students to become fully immersed in virtual scenarios where they must participate in scientific processes, or partially immersed where they use mobile technology to aid a real-life investigation. Barab & Dede (2007) highlight the use of game-based technologies to target academic content learning in more embodied and integrated formats.

Zurina & Williams (2011) helped my understanding of embodied learning in the classroom by analyzing how children may gesticulate to solve problems they are working on. Gesticulation is required by these children as they work integrated with their bodies and environment in the learning process. Without realizing it, as I explored this topic I realise that I model embodied behaviour to my students through instruction. For example, when analysing linear and polynomial graphs, I teach students to use the left arm rule to determine if the leading coefficient is positive or negative (one holds their left arm up to recreate slope of the graph; if it is easy the slope is positive, if it is difficult to contort your arm in such a way the slope must be negative).

Are there other embodied actions which help teachers reach their students?

Are you performing gestures which aid in learning without knowing it?

Barab, S., & Dede, C. (2007). Games and immersive participatory simulations for science education: an emerging type of curricula. Journal of Science Education and Technology, 16(1), 1-3.

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. Retreived from: http://www.hitl.washington.edu/people/tfurness/courses/inde543/READINGS-03/WINN/winnpaper2.pdf

Zurina, H., & Williams, J. (2011). Gesturing for oneself. Educational Studies in Mathematics, 77(2-3), 175-188.

Synthesis

TELES present an engaging and relevant way to encourage student learning. In Module B, we took a look at four models of TELEs which help to bridge the gap between real-world science and the textbook-based model many science teachers have become reliant on. These models are T-GEM, Anchored Instruction, SKI/WISE, and LfU. Some of the major similarities between these models are collaborative approaches, solving real-world problems, working with real-world data sets, and scaffolding new observations with student preconceptions. Such approaches strive to break the cycle of textbook and fact-based learning which do little for generating a realistic view of science, motivating students, and developing critical thinking skills. The exploration of these four models has greatly increased my confidence in building an effective science classroom. These topics are a refreshing way to integrate high level cognitive skills into a science classroom and will greatly aid my ability to design and run science classes which can remain interesting, relevant, and applicable to students.

While working through this module I still struggle to think of creative ways to include these four models into my mathematics instruction. Apart from data analysis and the use of math in multi-disciplinary problems, I remain lost in how to change my mathematics instruction in similar constructive ways as in science. I find the access to online Math instructional aides to be limited in ability and scope. As the new BC Ministry of Education rolls out to all the grades, learning activities such as the four which we looked at in this module will become valuable assets to fulfill curricular and core competencies.

Cognition and Technology Group at Vanderbilt (1992). The Jasper Experiment: An exploration of issues in learning and instructional design. Educational Technology, Research and Development, 40(1), 65-80.

Edelson, D.C. (2001). Learning-for-use: A framework for the design of technology-supported inquiry activities. Journal of Research in Science Teaching,38(3), 355-385. http://ezproxy.library.ubc.ca/login?url=http://dx.doi.org/ 10.1002/1098-2736(200103)38:3<355::aid-tea1010>3.0.CO;2-M

Khan, S. (2007). Model-based inquiries in chemistry. Science Education, 91(6), 877-905.

Williams, M. & Linn, M. C.(2002) WISE Inquiry in Fifth Grade Biology. Research in Science Education, 32(4), 415-436.

Shared Video: TELEs in the STEM classroom

Assignment #2, The Design of TELEs encouraged us to shared our videos. I produced a guide to PBL that finally gives a more research grounded basis for our STEM program. Very satisfying!

This video was designed to highly one aspect of the guide: The Role of Technology in STEM. I hope it provides concrete examples of how we can use the affordances of technology to transform how we teach.

I’ve enjoyed learning from you all. It’s been awesome to be in an environment for sharing so many good ideas. Enjoy the rest of the summer!

Michael

T-GEM, Illuminations & Angles

Model-based learning is a theory that allows students to learn from building, critiquing and changing our ways of thinking on how the world works (Khan 2007). One of the big ideas from BC’s grade 6 math curricula is: Properties of objects and shapes can be described, measured, and compared using volume, area, perimeter, and angles. I’ve decided to use a T-GEM model to support student inquiry while using an information visualization technology from a website called Illuminations. In particular, to examine the challenging concept of how the angles of shapes add up when manipulated. Triona and Klahr argued that computer simulations can be as productive a learning tool as hands-on equipment, given the same curriculum and educational setting (As cited in Finkelstein et al., 2005).

T-GEM Model for understanding how angles in shapes work:

Introduction: Students will be introduced to pictures of different environments: downtown of cities, houses, construction, buildings, cars et. What shapes do you see? Patterns? Can you determine the measurement of each angle? The sum of all angles within these shapes?

Generate: Using the following link below, students will choose a polygon and reshape it by dragging the vertices to different locations. The students will see that when the figure changes shape, the angle measures will automatically update. Are there any patterns? What relationships do you see with triangles, quadrilaterals, pentagons, hexagons? Does the sum of all angles change or remain the same when they are manipulated? They will record their observations down.

http://illuminations.nctm.org/Activity.aspx?id=3546

Evaluate: Find a formula that relates the number of sides (n) to the sum of the interior angle measures. Why are we learning this? Can you think of any real-world examples when you would need to know the different angles within shapes? On the applet, play the animated clip in the lower right corner. Is there a different result for different shapes? Compare your results with a different group. Did you find the same formula?

Modify: Can you now look at shapes and determine the sum of all angles? Are there instances where you are not sure? Do shapes must be a certain size?

Reflection: Students will revisit the simulations again and create their own shapes. They will test their peers by asking them questions such as, “What will happen to sum of all angles when I move vertices upward?” They will reflect on their findings by posting a response to a collaborative tool called Padlet.

Finkelstein, N. D., Adams, W. K., Keller, C. J., Kohl, P. B., Perkins, K. K., Podolefsky, N. S., … & LeMaster, R. (2005). When learning about the real world is better done virtually: A study of substituting computer simulations for laboratory equipment. Physical Review Special Topics-Physics Education Research, 1(1), 010103.

Khan, S. (2007). Model‐based inquiries in chemistry. Science Education, 91(6), 877-905.