Kayla Ney SNERP Presentation

Kayla Ney Author

07/28/2020 Added

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My project was looking at engineering student reflections for patterns in study planning across the semester.

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[00:00:01.480]Hello, my name is Kayla Ney
[00:00:02.860]and my project this summer was looking
[00:00:04.320]at first-year engineering student reflections,
[00:00:06.820]evaluating their plans and actions
[00:00:08.540]for meeting course learning objectives.
[00:00:10.510]I'm gonna go to the next slide
[00:00:11.590]so that these are a little bigger.
[00:00:13.720]So the reflections have been shown
[00:00:16.030]to be a powerful tool for students.
[00:00:17.530]It can incite and metacognitive process
[00:00:19.470]or help students think about the way they're thinking
[00:00:21.900]and aid in planning for the future
[00:00:23.550]and improve performance in the course.
[00:00:25.740]Standards-based grading is an educational strategy.
[00:00:28.310]It focuses on providing students
[00:00:29.790]with clear learning objectives statements,
[00:00:32.040]and then the feedback students are given on assignments
[00:00:34.610]relate to those specific objectives.
[00:00:37.830]When you pay reflection and standards-based grading,
[00:00:40.520]this can help students use those stated learning objectives
[00:00:43.690]in the feedback they're given to gauge their level
[00:00:45.610]of understanding of the course.
[00:00:47.840]The effects of reflection on student planning
[00:00:49.570]are not well-defined in the context
[00:00:51.040]of engineering education, which brings us to our purpose.
[00:00:54.100]The purpose of this investigation was to explore the effects
[00:00:56.890]of reflection and standards-based grading feedback
[00:00:59.064]on first year engineering students' plans of actions
[00:01:01.960]for improving their learning
[00:01:03.440]regarding those stated learning objectives
[00:01:06.230]across the semester.
[00:01:10.790]The way that we went about this study
[00:01:12.140]was looked at six sections
[00:01:14.140]of a first year engineering spring semester course.
[00:01:16.940]They were given reflections to complete in class
[00:01:19.080]immediately after they turned in an assignment
[00:01:21.440]from the previous week
[00:01:22.580]and before they were given the new assignment.
[00:01:25.380]These reflections were online.
[00:01:27.010]They included multiple choice items
[00:01:29.060]that asked the students to self-report
[00:01:31.030]what actions they had taken
[00:01:32.450]to understand the learning objectives
[00:01:34.140]when completing the previous week's homework
[00:01:36.550]and what actions they planned to take
[00:01:38.540]to improve their understanding
[00:01:39.940]of the learning objectives over the next week.
[00:01:42.610]Possible responses to these questions
[00:01:44.259]consisted of 14 options that we parsed
[00:01:46.720]into three categories.
[00:01:48.320]The first category are ask related actions.
[00:01:51.020]So asking classmates, the instructional team,
[00:01:53.210]going to office hours.
[00:01:54.730]The next category involves researching and reviewing.
[00:01:57.580]So reviewing a student's performance,
[00:01:59.513]reviewing their learning objectives,
[00:02:01.240]solutions to problem sets or online modules,
[00:02:03.900]and researching via Google or MATLAB's help function.
[00:02:07.460]The last category practice and check uses feedback
[00:02:10.630]and old practice questions
[00:02:13.010]as well as exploration activities the students were given.
[00:02:16.860]Reflections were gathered online nine times
[00:02:18.870]over the semester and they regarded the completion
[00:02:20.714]of problem sets two through 10 of the course.
[00:02:24.470]The number of planned and completed actions
[00:02:26.059]selected from each category
[00:02:27.660]were visually compared over time to look for changes
[00:02:30.060]in type and amount of actions and plans selected.
[00:02:34.320]So here are our results.
[00:02:35.700]Just to explain these graphs a little bit,
[00:02:37.290]if I can draw your attention to the orange graph,
[00:02:39.860]the brighter color are the planned actions,
[00:02:42.550]the number of planned actions normalized
[00:02:44.850]to the number of students in the course.
[00:02:47.180]And then you also see that the x-axis is problem sets,
[00:02:52.010]so that's problem sets two through 10.
[00:02:53.920]You can think of these as weekly as well.
[00:02:56.410]The more dull color were the completed actions.
[00:03:00.810]So, you can see right off the bat that in every category,
[00:03:04.800]there are more planned actions than completed actions.
[00:03:09.750]Something we also did for all of these sets
[00:03:11.710]is that using a chi squared test,
[00:03:13.520]we saw that the P values were all less than .05,
[00:03:16.800]indicating a significance of answers over time.
[00:03:21.700]So in all of these graphs,
[00:03:23.440]something to note is that the planned and completed actions
[00:03:27.090]generally start with a bit of a gap
[00:03:28.440]at the beginning of the semester,
[00:03:30.000]they approach each other in the middle of the semester,
[00:03:32.330]they get closer in value,
[00:03:33.860]and then a gap reopens at the end of the semester.
[00:03:37.370]Gap closure could be due to students
[00:03:39.600]refining their study actions
[00:03:41.780]and the gap reopening could be due to finals week.
[00:03:44.170]We will now look at each of these individually.
[00:03:48.090]Within the asset category,
[00:03:49.430]we can see that the smallest gap was in problem set nine.
[00:03:52.270]This is later in the semester,
[00:03:53.540]so perhaps students have figured out what helps them.
[00:03:57.060]The largest gap was problem set 10,
[00:03:59.570]but also closely followed by three and four.
[00:04:02.570]In problem set 10, this could be like previously mentioned
[00:04:05.410]due to finals week, and three and four,
[00:04:06.899]since they're at the beginning of the semester,
[00:04:08.730]could just be due to students figuring out the course
[00:04:11.180]and what helps them.
[00:04:13.710]Overall, if you look at the planned and completed actions,
[00:04:16.760]they both increase over the semester,
[00:04:19.160]which kind of leads to the belief
[00:04:21.270]that these activities were at least something
[00:04:24.290]that students felt helped them
[00:04:26.530]or felt was valuable in their goals for the course.
[00:04:30.630]In the research and review category,
[00:04:32.250]the smallest gap that we see is in problem set nine
[00:04:34.790]and the largest gap we see is in problem set three
[00:04:37.950]closely followed by problem set 10.
[00:04:39.730]This is similar to what we saw in the ask category.
[00:04:43.170]So talking about the trends a little bit,
[00:04:44.710]we see planned actions decrease over the semester
[00:04:47.250]and completed actions decreased slightly,
[00:04:50.120]but largely do stay the same on average over the semester.
[00:04:54.320]There are a couple possibilities for this,
[00:04:56.060]we'll go into elucidating a few in the future works
[00:04:58.730]but I did want to bring up one idea.
[00:05:00.179]If planned actions are decreasing,
[00:05:02.178]this could be due to the fact that students
[00:05:05.120]at the beginning of the semester plan to do a lot.
[00:05:07.550]As the semester goes on,
[00:05:08.900]they find the core activities that they like.
[00:05:12.420]And as they realized this relationship,
[00:05:14.013]they stopped reporting the actions
[00:05:17.170]that they hadn't been doing previously.
[00:05:20.430]Now we have the practice and check category.
[00:05:22.700]We see that the smallest gap in this category
[00:05:25.530]was problem set seven.
[00:05:26.610]It isn't the middle of the semester.
[00:05:27.740]It could be when students are figuring out
[00:05:29.560]what they need to do.
[00:05:30.740]The largest gap is problem set three.
[00:05:32.620]Now it looks like initially that problem set three
[00:05:35.120]has the biggest gap we've seen thus far.
[00:05:36.930]However, if you note the scale on the y-axis,
[00:05:39.510]we can actually see that people,
[00:05:41.160]students are just choosing less practice and check actions
[00:05:44.760]as compared to researching and reviewing and asking.
[00:05:47.550]This could be due to the fact
[00:05:48.810]that practice and check activities likely would take longer
[00:05:51.160]since you're doing more problems.
[00:05:53.630]Another reason for this gap could be that students
[00:05:56.020]did not have a lot of feedback quite yet,
[00:05:58.070]so they were planning on doing the activities
[00:06:00.280]and then they realized they didn't have feedback
[00:06:01.810]or they didn't have practice problems to actually complete.
[00:06:05.370]So trends that we see, the planned actions
[00:06:07.220]decrease over the semester, like the research and review.
[00:06:09.610]This could be due to refinements
[00:06:11.197]and the completed actions increase.
[00:06:13.580]This could be due in part to the fact that,
[00:06:15.700]like I said earlier, students did not have the feedback
[00:06:17.730]or the practice problems yet,
[00:06:19.230]or the fact that these actions are becoming more relevant
[00:06:22.700]as the semester goes on.
[00:06:25.440]So our conclusion is that we can definitely see
[00:06:27.990]that student study action selections
[00:06:29.760]do change significantly over time.
[00:06:32.050]To further elucidate this relationship,
[00:06:34.164]we wanna look at couple things.
[00:06:36.160]We could start with, within each category,
[00:06:38.700]we wanna evaluate the frequency of actions.
[00:06:40.840]So do the students select the zero, one, two, or three items
[00:06:44.840]in a category or more depending on the category?
[00:06:47.720]And how did that change,
[00:06:49.620]A, between planned and completed actions
[00:06:51.510]and then also how did that change over time.
[00:06:54.010]We also wanna look at individual planned action choices
[00:06:57.110]versus the choices they completed
[00:06:58.791]to see which individual actions were complete, incomplete,
[00:07:02.900]and we could also see preferences
[00:07:05.340]if we investigate this way.
[00:07:07.350]We also wanna evaluate individual choices
[00:07:09.370]to see which actions may simply have been necessary
[00:07:12.130]to actually turn in the homework.
[00:07:13.870]So for example, maybe a student said
[00:07:15.980]that they asked a peer a question
[00:07:17.330]but that question could have simply been,
[00:07:18.947]"Hey, how do I turn this assignment in?"
[00:07:20.970]And we want to separate those out from actions
[00:07:23.700]that students were taking to actually help themselves
[00:07:26.410]with the learning objectives.
[00:07:27.773]That relationship is very important to distinguish
[00:07:30.280]if we wanna get more data out of the study.
[00:07:33.300]We also could evaluate class action variability
[00:07:35.960]to see if the professor had any sort of impact
[00:07:40.160]on selected actions and students.
[00:07:42.890]And another interesting thing going forward
[00:07:45.170]is we could compare this data to a similar study.
[00:07:47.840]In this study, reflections were actually handwritten
[00:07:50.300]so they were simply asked, what did you do last week,
[00:07:52.910]what will you do next week,
[00:07:54.080]in regards to the learning objectives,
[00:07:57.200]and they were not guided with these 14 choices,
[00:07:59.670]so this could help us investigate
[00:08:01.310]our method of data collection.
[00:08:04.550]I would like to thank the NSF and the SNERP program
[00:08:07.390]for allowing me to complete this project this summer.
[00:08:10.330]Thank you for listening.

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