Scientific practices in teacher education: the interplay of sense, sensors, and emotions

Figures & data

Table 1. Teaching sequence

Activities	Description/justification	‘Benzen Ring corner’
Activity 1. Usually, when it snows, the snowplough adds salt to the roads. What do you think that happens? Describe it.	As learning is essentially a matter of creating meaning from the real activities of daily living, with this activity we try to embed the scientific contents in the ongoing experiences of the learners, creating opportunities for learners to approach these contents in the context of real-world challenges. In that way, contextualizing in a real-world situation, learning occurs from the classroom through the real practice experience and vice versa, so that students get involved in looking for explanations of known phenomena that make sense to them. The answers that usually appear are: to increase friction and prevent cars from slipping, to melt the snow … Then the students are asked for more concrete responses through the following question.	Real World
Activity 2. What do you think will happen to the temperature when salt is spread? Make predictions and justify them.	The purpose is to make them predict and justify their ideas. Although all kind of responses are given, the most common is the temperature increases, because salt is corrosive, or because it produces a chemical reaction that dissolves the snow … Now we wonder if these models or explanations are useful for us to explain their predictions.	Prediction
Activity 3. Do your explanations or models really explain what happens to the temperature?	To deepen the need to justify their hypotheses so that they were coherent and explained their predictions properly, we propose using not just experiential knowledge, and so we introduce Kinetic Molecular Theory (KMT). This can be used as a model that can be useful to justify their initial hypotheses, so we wonder if this model serves to explain the phenomenon. Ideas related with temperature and its relation to the vibration rate of molecules, phase changes, etc., are discussed in the classroom. After this necessary debate, and once they have explicitly formed their models to explain what they think happens to the temperature and why, we proceed to look for evidence, focusing the inquiry on the temperature variation.	Explanation
Activity 4. How would you know if your hypothesis fits the reality? Let’s look for evidence to check it!	The students are asked to suggest different ways to measure the temperature. This is a good opportunity to work on some strategies of scientific enquiry such as the need to keep one factor constant and vary the other when controlling variables, etc. After listening to their proposals, we can show the materials that we have to operationalize a design and implement it in the classroom: glasses with ice, salt, temperature sensors connected to the computer. As well as considering and collecting data of temperature, we have to find out if the salt effect supposes an increase in the speed of melting ice, so to prepare a ‘blank solution’ with a sensor (a glass only with ice) is proposed.	Data
Activity 4.1. Represent graphically your prediction on the variation of the temperature when salt is added to ice	Before data collection, the software used allows for graphic representation of the evolution estimated for the variable. It may be interesting to work on the interpretation of graphs from the different predictions, identifying the initial temperature (ice temperature), the melting/freezing temperature, the evolution expected (increases or decreases), and the rate of change, the highest/lowest temperature it will reach …	Prediction
Activity 4.2. Collect data to test hypotheses	Furthermore, Science includes activities such as experimentation and/or observation, which enable the generation of data and subsequently the consideration of evidence to construct and review models and explanations (Easterday, Rees Lewis, and Gerber 2016). Therefore, students implement the experimental design and initiate the data collection, which will be represented numerically and graphically in real time. Students perform ‘Activities’ using sensors and obtain results that compare with what they thought initially	Activities
Activity 5. Observe what happens and analyze the results	Does the ice (without salt) melt before the ice with salt? Does the ice temperature go up or down when you add salt? At what temperature? What temperature does it reach? What about the amount of melted ice: Does the glass only with ice has more liquid water or it is the one with ice plus salt? Does it match what you expected? Once the PSTs observe the results in the graph, we propose these questions to facilitate the data analysis. The sudden drop in temperature (which can reach −17.4°C) generates a big surprise, especially when comparing the results with what they predicted. The drop-in temperature when salt is added is short, but data should be collected for 5 or 10 minutes until the minimum temperature is reached. Again, the participants are surprised by the fact that despite the sudden drop in temperature, the glass with the mixture ice-salt has almost completely melted while the glass only with ice has not.	Data
Activity 6. Synthesis and review of explanations	The evidence obtained from data can contribute to review their explanations and/or models. Then, if the salt lowers the temperature of ice water to −10°C, why is salt added when it snows? As the majority hypothesis is that the salt ‘melts because it increases the temperature of the snow,’ when they realise that it actually decreases, it becomes necessary to justify why salt is added to snow and to rethink an explanation of what happens. Indeed, salt is sprinkled because it makes ice and snow melt faster, but … why does this happen? We need a model that allows us to explain it. Now, we begin a new section (explicative) about the question: Why salt facilitates the ice melting at so low temperatures?	Explanation
Activity 7. Can we explain why salt facilitates the melting of ice at such low temperatures? We need a model to explain …	These comments are developed below, in the main text of the document.	Model
Activity 8. This SensoPill, has made sense to you? What have you learned and felt?	Self-regulation of learning and emotions are performed. More comments are added in the main text of the document.	–

Table 2. Description of the sequence

Expression of ideas	Item 1. Express initial hypotheses: Why do you put salt on the roads when it snows? Item 5. Represent hypotheses graphically and interpret
Use of models	Item 2. Justify hypothesis Item 3. Molecular kinetic theory to explain changes in state Item 8. Model to explain why the temperature drops when adding salt Item 9. Make predictions for other similar phenomena
Look for evidence	Item 4. Design experiments to test hypothesis Item 6. Data collection with the temperature sensor Item 7. Analysis of data (coincidences and discrepancies with hypothesis)
Didactic reflection	Item 10. Teaching approach by inquiry and modelling Item 11. What is science and how is it constructed?

Figure 1. Average of the responses provided by the participants for each category (showed in Table 1) of the questionnaire KPSI (‘how much’ they knew before and after experiencing the teaching sequence)

Figure 2. Evolution in the average of the responses provided by the participants for each category (showed in Table 1) of the questionnaire KPSI (‘how much’ they knew before and after experiencing the teaching sequence)

Figure 3. Emotions experienced by the participants

Figure 4. Participants’ perceptions about the importance of using models in scientific practices before and after implementing the teaching sequence

Easterday, M. W., D. G. Rees Lewis, and E. M. Gerber. 2016. “The Logic of the Theoretical and Practical Products of Design Research.” Australasian Journal of Educational Technology 32 (4): 125–144. doi:10.14742/ajet.2464.

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