challenges students to make and defend their predictions,
uses both mJiTT* (modified Just in Time Teaching) and TPS* (Think-Pair-Share) teaching approaches,
is interdisciplinary in scope as it involves the application of chemistry to oceanographic concepts, and
encourages the instructor to have students to organize data.
Let's begin. (*: But of course, use whatever best suits your teaching style and classroom dynamics.)
This classroom activity introduces the concept of salinity (mJiTT if needed) and tasks students to predict the range of salinities in certain regions of the ocean (coastal and open water, all four hemispheres, high and lower latitudes). The next post will describe an classroom activity where students predict the buoyancy of a merchant cargo ship based on seawater surface salinity measurements. But first they need to know something about the salinity of Earth's vast oceans. To encourage the continuous exchange of ideas, students discuss their predictions and reasons for their predictions with others using TPS or some other interaction/idea exchange format. This can be done individually and/or in groups.
An authentic look at the world and its connected physical and chemical systems requires an interdisciplinary viewpoint. Let's put this into practice in Question 1 (Q1) below. Students may ask what is salinity. My response- look at the units! Or perhaps you may want to elaborate more on salinity via some mJiTT lecture or video. Whatever you choose, students now have a working foundation about salinity and are now ready to apply this knowledge to Q1.
Q1: Each of the five lettered rectangular regions (all of the same area) on the Mercator map projection of Earth represents an average seawater surface salinity for a given year or month. Per the information below it can be deduced that, according to this data set, the measured surface salinity values for Earth's oceans range from less than 32 to greater than 36. Based on prior knowledge and/or on educated speculation, in the table match the average seawater surface salinity value for each represented region. Note that different regions can share the same average salinity range.
When done compare your match prediction for each region with another student(s). Reconcile, if possible, any differences in predictions for each region.
Note to instructor: Larger versions of the map above (including a black and white version) are found in the Supporting Information document.
Commentary about this question: Realistically, I don't expect students to 'get it right'. That is not the point of this question. The point is to get students to think about the possibility that some oceanic regions may be more or less salty than other regions. And there must be a reason or reasons for the differences. It is the springboard to launch into applying chemistry concepts to understanding the physical world, which is knowable.
Any prediction (ideally) is based on a reason or reasons so let students defend their individual prediction and reason(s) for it in a classroom discussion format. Once done students discuss their predictions with other students using TPS or other engagement/idea exchange format. Or as discussed in detail in the second post of this series another option to encourage student interaction, discussion, and idea sharing is to have students organize into 'Choice groups'. All students who predicted a salinity value <32 for region A get into a group, students who predicted 32-34 for region A get into another group, and so on. Then each 'Choice group' discusses amongst themselves (intra-discussion) reasons for their salinity value prediction for region A (or whatever region). After the intra-discussion 'Choice group for <32' for region A chats with 'Choice group for >36' for region A. Perhaps some students will exchange the group they are in after discussion.
New data- What to do with it?: As mentioned in the previous blogs in this series, a common theme/approach in my teaching is tasking students with organizing data. Q1- like most any question- provides an opportunity for students to organize new aggregate (= class) data and search for data patterns. First, students need to figure out the identity of the new data based on their predictions. And second, once identified, organize the new data generated from their predictions. Let students figure out whether to use tables, pie charts, bar graphs, and so on. Figure 1 below is a possibility that students may propose in organizing the aggregate class data. The numbers are hypothetical for a class size = 24 and are for illustrative purposes.
Time permitting or as a take-home assignment, have students write a short Results paragraph summarizing the aggregate data. Below is an abridged sample Results summary for the hypothetical data in Figure 1.
Sample Results summary: In Figure 1 for region A 83% [(20/24) x 102] and for region E 88% [(21/24) x 102] students predicted that the two higher latitude oceanic regions A and E will have a lower surface salinity (<34 maximum) than the three lower latitude oceanic regions B (50%, 12/24), C (17%, 4/24), and D (38%, 9/24). Student predictions indicate that regions C and D, located in warmer, lower (near equatorial) latitudes in the southern hemisphere are predicted to have higher surface salinities (>34) than regions A and E in colder, high latitudes of both hemispheres. Interestingly, for regions C and D, which are both located in the same hemisphere and at the same latitude but different longitudes and oceans, student predictions indicate a higher percentage of prediction for surface salinity values > 34 for region C than for region D, 83% (20/24) versus 63% (15/24) respectively. Region B, which is located more or less the same latitude from the equator as regions C and D but in the northern hemisphere, is predicted to be more saline than regions A and E. But compared to regions C and D, students predictions for region B to have a salinity >34, is less (50%, 12/24 for B versus 83% and 63% for C and D respectively). And so on it could continue....
Another option from the instructor vantage point is to ask students questions about the new data they just organized. Below is an example of a question derived from the hypothetical data in Figure 1.
Q: Based on the (hypothetical) data in Figure 1, compute for each region the percentage of students that predicted the average seawater surface salinity value to be less* than 34. Complete the table below.
*: or >34 or <36 and so on...
Predictions vs. Actual Data: Agreement? Student salinity predictions are made based on some level of prior knowledge or maybe many just guessed. Regardless, each prediction is or is not in agreement with actual data. So how do student predictions match up with actual data? Let's find out.
Q2: Below (Figure 2) is a color contoured map showing measured annual mean sea surface salinity levels. Compare your predicted salinity value for regions A-E in Q1 to the actual measured value (for this data set). When done complete the table below the map.
Note to instructor: A larger version of the salinity map below is found in the Supporting Information document.
So how did student predictions pan out? Figure 3 below represents the match/does not match results from the hypothetical data in Figure 1. In the first blog post I discussed the idea of inquiring students about confidence in their predictions.
Aggregate the prediction data and have a brief discussion about the results (example, Fig. 3 above). Why did so many students predict so poorly for regions A (match = 12%, 3/24) and B (match = 33%, 8/24)? What real world factors were not taken into account that led to a poor prediction percentage? Below are some factors that students may propose/discuss:
region A- comparatively high frequency of precipitation (rain/snow) + low evaporation on account of cool annual air temperatures + cold surface water + cloudy skies + Alaskan and British Columbian rivers with large amount of water from snowmelt and persistent spring/summer rains flow into this oceanic region (Gulf of Alaska);
region B- comparatively low frequency of precipitation (= the 'horse latitudes') + high evaporation on account of warm annual air temperatures + warm surface water + cloudless, sunny skies (leads to high incidence of absorption of solar radiation by surface waters)
Of course, the above can be applied to any region where students were asked to predict. Another teaching/learning option (= opportunity) is to have students correlate confidence in a prediction with correctness of the prediction. Have them figure out any patterns or reason(s) for being Somewhat Confident versus Confident.
In closing, what's in store for the fourth blog post in this series? Conclusions drawn from Observations 1-4 described in the first two posts plus the concept of salinity described in this post serve as the foundation for interpreting and applying seawater surface salinity data to the buoyancy and hence stability of a merchant cargo ship as it sails through oceanic waters of different salinities and temperatures. A ship's master needs a working knowledge of the salinity zones that the ship under the master's command sails through.
Thanks for reading....