Helping students uncover the concepts studied in the physics classroom is the goal of every teacher. The methods evolve and technology modifies the way the science is taught. Technology modifies the pedagogy. It can be an excellent aid to learning, but I sometimes wonder if students really ‘get it’, or if they are just pushing buttons on their calculators. It is a well accepted fact that not everyone learns the same way, and that each student is an individual with individual preferences regarding how they learn best. There are two ways to deal with this. Teach each lesson with elements directed at each individual preference. Or teach with a style that, hopefully, has commonalities with the greatest number of individuals.
Physics has, in my opinion, been much more attentive to this fact than other sciences in recent years. The modeling curriculum seems to be a result of this. Integrating as many different ways to represent information and core concepts as students are comfortable with is probably the safest approach to teaching to the greatest number of individual learning styles and reaching the greatest number of students.
Purpose
My main purpose in testing new classroom methods is the same as any other teachers. I wanted to increase student understanding of concepts in physics in an effort to increase student performance on standardized tests. I wanted to do this in a way that incorporated new technologies. I also needed to do this in an inexpensive manner.
In order to determine if student performance increased I used two different tests as my benchmarks. The first was the Force Concept Inventory (FCI) and the second was the New York State Regents Exam in Physical Setting / Physics.
I also examined two different types of hardware and software to deliver the ILD content. The first year I used Vernier hardware with Logger Pro software. The second year I used Pasco hardware with its accompanying hardware. Both sets of materials introduced students to new technology which they had never encountered before.
The final factor influencing my investigation was financial. ILDs use a single set of both hardware and software to establish situations, elicit predictions and provide realtime feedback results for students. A single setup required to run the ILDs is about 500 dollars. The cost of running similar activities in small lab groups would be 500 dollars per group. This does not include the cost of computers.
Description
Interactive Lecture Demonstrations are relatively simple labs to run. The always begin with simple descriptions of apparatus and expectations. Students are passive participants as the instructor runs several scenarios in each session. The scenarios all follow the same format, making for a very streamlined process once students and teachers have become accustomed to the protocols.
Once the students have been shown the apparatus (motion sensor, force probe, data logger, fan carts, track, and pulley) they are shown a specific type of motion and asked to make graphic predictions. These predictions are qualitative only. They begin with simple position-time graphs and evolve to include velocity-time graphs, acceleration-time graphs and force-time graphs.
Student knowledge is always elicited after seeing a specific demonstration. They are asked to fill in graphs on a “predictions” sheet. Once they complete their prediction of a specific demonstration, they are shown the demo again, with all electronic data collection active. This generates real time collections of the data they were asked to predict. We then discuss briefly any differences between predicted and observed patterns. Students then fill in graphs on the “results” sheet, which is identical to the predictions sheet.
Each ILD lab is a collection of four or five related scenarios which are designed to challenge student pre-conceptions through this method of elicitation and rapid feedback.
The first lab students are introduced to is based on human motion. The only new apparatus they are introduced to at this point is the motion detector and the data collection device. In my case I linked the motion sensor directly to a laptop computer. This piece by piece introduction of equipment has the effect of allowing the students to become familiar with single pieces of new technology one at a time. Later labs use more apparatus in concert, and would probably have students wondering more about the devices and their operation, than the forces being observed.
The first motion is simple. The instructor moves steadily away from the detector and students predict a position-time graph. After the predictions are done the motion is repeated with the equipment collecting data and the students see the actual graph the motion produced. The second motion is moving steadily towards the detector and students again predict a position-time graph. Again, students are shown the motion again and the resultant graph.
The second set uses the same two motions (walking towards and away from the origin) but students are asked to predict velocity-time graphs, then shown results.
The third set uses the same two motions but at approximately twice the speed of the first two sets. This time they are asked for both position-time and velocity-time graph predictions. They are again shown results. At this point we discussed differences in the two sets. How did the slopes differ with the position-time graphs? What were the differences between the two velocity-time graphs. Students could easily identify greater slopes on the second set, and relate it to the greater magnitude of the line on the velocity-time graph.
The final motion combines the two different motions. Move away from the detector at a constant velocity for six seconds, pause for six seconds, then move towards the detector at a constant velocity for six seconds. They make predictions of both distance-time and velocity-time graphs, then see results.
This first lab is an excellent introduction to the capabilities of the apparatus. The first motion is so simple the nearly all the students are able to accurately predict the graph. Once they see the graph, they become confident that the device is making an accurate picture of the motion. In more complex motions, when their predictions do not match the collected results, they trust the equipment, rather than argue for their incorrect predictions.
The second ILD introduces a low friction motion cart and track set up. It also introduces a fan unit.
The first set is nearly identical to the motions from the human motion ILD. First the cart is given a push away from the detector and they predict position-time and velocity-time graphs. They are again shown results.
The second set has the cart pushed towards the detector and they predict position-time and velocity-time graphs. They are again shown results.
Important to both is the explicit instruction that they are only predicting the motion of the cart after the push. These are an excellent segue from the previous activity. The motion is the same, and they are again allowed time to become familiar with new apparatus.
The third motion introduces the fan unit. They are also now asked to make a prediction of an acceleration-time graph. The fan cart is turned on near the detector and moves away at an increasing velocity as the fan provides positive acceleration. They only predict the graph while the cart is moving on the track. The make both velocity-time and acceleration-time graphs.
The fourth motion challenges students to predict velocity-time and acceleration-time graphs as the cart moves towards the detector with the fan unit slowing the cart down.
The next motion is very similar to the previous one. The difference is the cart is allowed to continue its motion. The fan motor causes the cart to slow, stop, and move away from the detector.
The final demonstration in this lab is a replication of the last motion, but instead of using the fan as the source of acceleration, the track is tilted. The detector is on the top of the slope and the cart is pushed up towards the detector and allowed to stop and return to the bottom of the track. This is very effective in introducing gravity as a source of acceleration. Students a comfortable with how the fan is effecting the cart by the end of the lab. Once they see similar graphs produced by the effect of gravity, they can be comfortable with the graphs that motion created by acceleration due to gravity produces.
The third ILD introduces a pulley, string, and mass set up to provide the cart with acceleration. It also introduces the force probe. The probe is attached to the cart and the string is tied to the hook on the force probe. The string runs over the pulley and the mass hangs freely. It is apparent to students in this activity that the mass is doing the pulling. It is not immediately that gravity is the responsible force.
The first demo has the cart moving away from the detector pulled by the pulley/string/mass set up on the other end of the track. Students create velocity-time and acceleration-time. They also predict applied force-time and net force-time graphs. This is their first exposure to force-time graphs and requires some additional time to both predict and discuss the results.
The second demo adds a friction pad and the same graphs are generated. They are also asked to discuss what the graphs would look like if the friction were great enough to prevent the cart from moving.
The third removes the string/pulley/mass system and replaces it with two fan units acting against one another. They only predict velocity-time and acceleration-time graphs.
Demo four reattached the force probe and it is pulled by hand away from the detector. Students are asked to consider the pull of the hand when they predict their force-time graph, as well as velocity time and acceleration-time graphs.
The fifth demo in this set reattaches the string/pulley/mass system. The cart is pushed towards the detector as the mass provides acceleration away from the detector. The cart slows then stops. They then predict velocity-time, acceleration-time, and force-time graphs.
The final demo is the same as the fifth demo, but the cart is allowed to continue it motion pushed toward the detector, pulled away by the pulley system, the cart slows, stops then changes direction and moves away from the detector. They again generate the velocity-time, acceleration-time and force-time graphs.
ILD four uses two force probes and two carts or two probes and two blocks. The lab is designed to illustrate Newton’s third law. This lab is different in that it does not ask students to draw graphic predictions. Students are asked to compare what graphs would look like verbally only.
The first three demonstrations use a probe to push a block with a probe attached. The first is at constant speed, the second is speeding up, and the third is slowing down. The authors recommended doing all three in series together, in the interest of time. The force-time graphs each probe generates are compared side by side after they have made their predictions.
The fourth demo uses two people pushing carts with probes attached. The carts are pushed together, and moved first to the right, then to the left. They are asked to compare what each probe will show, and results are then examined.
In the fifth demo one cart is rested against a barrier and the other is pushed against it. This is not meant to be a collision. Students discuss what the graphs will show, and then check results.
The final three demos are collisions. The first is between two carts with the same mass moving towards each other. The second is a high mass cart moving, striking a stationary low mass cart. The third is a low mass cart moving, striking a stationary high mass cart. The elicitations are not graphic, but are asking comparisons of the forces experienced by both carts. It is even suggested to label one object ‘truck’ and the other ‘compact car’ to help tie this activity to student experience. Ultimately this ends up illustrating the forces experienced are equal and opposite.
The final ILD I performed was a return of the cart on the ramp using only the
motion detector. It introduces the idea of energy. It treats both kinetic and potential energy. The set up is the same in every case. The ramp is inclined with the motion detector located at the bottom. In all cases the same motion is considered as well. The cart is pushed up the incline away from the ramp, allowed to stop, and roll back towards the detector.
Demonstration one asked for the now familiar position-time, velocity-time, and acceleration-time graphs. Students have already seen a similar demo and the resultant graphs in the final demonstration of the third lab.
The second demo uses the same motion but asks students to predict a kinetic energy-time graph. They are also asked if the kinetic energy is ever zero, where is the kinetic energy greatest, and if energy is conserved.
The third demo does the same as the second but refers to potential energy. It asks for the graphs, and also responses to the same three questions.
The fourth demo is the same as the previous three, but now wants a graphic prediction of total mechanical energy.
The final demonstration adds friction to the cart. It is pointed out that friction can no longer be ignored. The cart is pushed, the motion observed, and the students predict velocity-time, acceleration-time, kinetic energy-time, potential energy-time, and total mechanical energy-time graphs. They then get to view the actual graphs generated by the apparatus.
In all cases students were asked to make predictions. In all cases they were also shown the actual graphs created by the probe ware. In all cases they transferred corrected graphical representation of observations onto results sheets.
I performed this set of lab demonstration in two consecutive years. I was fortunate enough to be able to use both the Pasco probeware and software, and the Vernier probeware and software. I experienced no significant differences in the quality and ability of the devices. The ranges, sensitivities, and sample rates of both are adequate for all of the exercises in this set of ILDs. The software is also well suited to the activities. Both offer very similar capabilities, with only minor differences.
Both Vernier and Pasco motion sensors experienced problems with detecting the fan cart. The fan generates ‘noise’ which can cause messy looking graphs. Several runs of some of the demos were required in order to acquire adequate graphical data. Neither system was any better or worse at dealing with the fan ‘noise’. There is definitely a need to discuss this with students both before and during the labs. Students need to be made aware that data collection in real world situations is not always neat and clean.
Before I discuss my results I must state that I have very small labs. The first year I only had a single lab class. I had not control group against which to measure my results. The second year I had the luxury of two lab groups, and could administer an alternative set of labs to a second group. The second group was shown parallel activities and allowed to use the probeware, instead of watching me operate it. The second group was not given the prediction/result sheets. They were asked to generate graphs of parallel activities themselves. The structure was similar to the rest of my lab program. The biggest problem I encountered with the group using the probeware on their own was their unfamiliarity with the associated software. Most of my students have never used a spreadsheet. I introduce them to spreadsheets early in the course anticipating the need for the throughout physics. However, they are uncomfortable with their use and did not easily explore their many uses.
Student performance on the Force Concept Inventory was not conclusive. There are some many other things which are taught and learned that there was no appreciable difference between students who experienced ILDs and students who did not. Both groups improved their FCI scores. Both groups showed good gain based on their pre-test scores. But there was no stand out area the FCI was able to illustrate to me as being more favorable to students who participated in the ILDs. The ILDs and the FCI are very different in nature. The FCI relies entirely on verbal cues and diagrams. The ILDs rely almost completely upon graphs of the relationships being witnessed. Granted that they both deal with the underlying concepts, the lack of graphical representation may explain why students who received ILD experience did not translate into better performance on the FCI.