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The graph below is a comparison of response rates to an online course evaluation in a college at Washington State University. Faculty had hypothesized that the response rate was limited by the amount of time the survey was open to students so the time available was varied in two administrations:
Fall 2007 11/23 to 12/22 (29 days) 10582 possible respondents
Spring 2008 3/31 – 5/5 (35 days) 9216 possible responents
The x-axis in the graph below is time, but normalized to the % of time the survey was open.
The y axis is normalized also, to the number of responses possible, based on course enrollment data, i.e., Y is response rate.
Figure 1 (click to enlarge)
The Fall 2007 survey ran to completion (reached an asymptote) where the spring one was (maybe) still rising at the cut off date. Fall and Spring total response rates are very similar, suggesting that more time when the survey is open has little impact on total response rate. So, contrary to what faculty hypothesized, the same overall response rate was achieved in a longer surveying window. This aligns with other data we have on response rates — there is some other factor governing response rate that is not yet identified.
Its interesting to note that you can see waves in the spring data, as if faculty exhorted students on Monday and got another increment of response.
Jayme Jacobson, Theron DesRosier, Nils Peterson with help from Jack Wacknitz
Previously we showed examples of how a transformed grade book (or Harvesting grade book ) might look from the perspective of a piece of student work seeking feedback. That demonstration was hand built and is not practical to scale up to the level of an academic program. Even the smaller scale use such as that suggested by a recent piece in the Chronicle entitled Portfolios Are Replacing Qualifying Exams would benefit from some automation. This post outlines a design for a software application that could plug into the Skylight Matrix Survey System (a new survey tool WSU is developing).
There are two workflows that we have previously described in general terms, one for the instructor to register assignments and another for students to request feedback as they work on those assignments. In the figure below we outline a series of screen shots for the instructor registering the assignment. During the registration process the instructor is matching the assignment to the assessment rubric dimensions that are appropriate. We view this registration of the assessments as a possible implementation of Stephen Downes’ idea in Open Source Assessment.
Instructor Dashboard (click image to enlarge)
This examples shows how an instructor might use a dashboard to register and monitor assignments. The workflow shows capturing the assignment, assigning rubric dimensions to be used for assessing both the student work and the assignment itself and ends with routing the assignment for review. This mock-up does not show how the instructor would see the student work created in response to the assignment, or the scores associated with that student work . The next step in the workflow would require an upload of the assignment so that it could be retained in a program-level archive. The assignment could be referenced from that archive for faculty scholarship of teaching (SoTL), as well as for program or accreditation review and other administrative purposes.
Once the assignment has been registered, the student could start from a student dashboard to request a review or feedback and guidance. We are differentiating the idea of a full review (with a rubric) from more informal feedback and guidance. This informal feedback would probably not be fed into a grade book but the captured feedback could be used by a student as evidence in a learning portfolio.
Student Dashboard (click image to enlarge)
The basic workflow for a student would let the student request a rubric-based review for a specific assignment in a specific course. The student would select the course, assignment, and other metadata. Once posted for review, the request would either be routed to multiple reviewers or the student would embed the review into a webpage using the HTML code provided. In the second step there would be an opportunity to upload a document. This might be used in cases where the document had no web incarnation (to give it a URL) or to “turn-in” a copy of the document that would not be subject to further editing, as might be required in some high-stakes assessments.
The Learning 2.0 model is supported in the last step, where the assessment is embedded in a web space still open to modification by the learner (as the previous examples illustrated)
Student-created Rubric-based Survey (click image to enlarge)
Students might want to use their own rubric-based surveys. This mock-up shows how workflow would branch from the previous workflow to allow the student to define rubric dimensions and criteria.
Student-created Simple Feedback Survey (click image to enlarge)
This last example shows how the student would create a simple feedback survey.
State of the Art
Presently there are several tools that might be used to deliver a rubric survey. The challenge is the amount of handwork implied to let each student have a rubric survey for each assignment in each course and to aggregate the data from those surveys by assignment, by course, and by student for reporting. A future post will explore what might be learned by having the data centrally aggregated. If there is value in central aggregation, it will have implications for the tool and method selected for the rubric survey delivery. The Center for Teaching Learning and Technology at WSU already has tools to make the handwork implied tractable for use in a pilot course of 20-30 students. We understand the path to develop further automation, but both pilot test and further automation require investment which requires further analysis of commitment, infrastructure, and resources.
Questions
1. Can this concept provide transformative assessment data that can be used by students, instructors, and programs to advance learning? In addition to assessing student learning, can it provide data for instructor course evaluation and for program level assessment and accreditation?
2. Can the process be made simple enough to be non-obtrusive in terms of overhead in a course’s operations?
3. Is it necessary to implement a feedback and guidance as a central university-hosted tool, or could students implement an adequate solution without more investment on the university’s part?
The following is an email exchange between Gary Brown, Nils Peterson of Center for Teaching Learning and Technology at WSU and members of the TLTGroup.
Ehrmann@ TLT: Nils, I’ve gotten a couple questions from a subscriber. Do any WSU colleges conduct student course evaluations exclusively online? All of them? What kind of response rate does WSU get to online surveys and what strategies seem to work best for that purpose?
Nils: WSU has several colleges that do online surveys exclusively (Engineering & Architecture; Agriculture and Natural & Human Resources; Pharmacy). Response rates vary by course from very low to 100%. Gary Brown can take up this conversation to talk about what we do/don’t know what drives response rates
Gary: As Nils notes, we have several colleges doing online evaluations, some exclusively, more joining all the time. Response rates vary, but maybe more importantly, so do the instruments and, more importantly yet, the way the evaluations are used. I won’t go into detail about the differences in the evaluations instruments we’ve encountered, but online or not, the quality and fit for a variety of pedagogies is for me much more of a concern than the mode of delivery. The way they are used extends validity, because response rates matter little if results are ignored by faculty, misunderstood or difficult to interpret, and, all too common, boiled down to a single number for ranking purposes. It is hard to make arguments about the validity of an instrument and process if it is all capped by use that is itself invalid. But that makes the more important argument—it isn’t response rate and subsequent issue of response bias that matters as much as it ought be making sure that the response is representative and appropriate for the purpose of the process—hopefully for improving students’ learning experiences.
All that aside, response rates:
In our College of Agriculture, the response rate was 53%. But that number varies widely across departments. Here is a picture of response rates across departments from about a year ago:
Needless to say, the variance across departments is mirrored by similar and dramatic variance among courses/faculty, so it is hard for us to attribute the variance exclusively to the medium of delivery. We make other conjectures in our analysis in an article we published a while back. A key to response rates, we note in the article, is that in department with the higher response rates, the chairs of the departments were involved in the design of the instrument and the decision to put it online. So there is something important to be said for leadership and the engagement in the process of that leadership. We also point to other associations with higher rates we tracked in certain classes, mostly associated with the engagement of faculty in the process, their demonstration throughout the term that they listen and respond (not necessarily capitulate) to students’ concerns, and that they work overtly to engage students in the teaching/learning/assessment process.
The issue is pretty hot, too, and there are a number of discussions about response rates:
http://www.utexas.edu/academic/diia/assessment/iar/teaching/gather/method/survey-Response.php
http://www.aapor.org/bestpractices
Most of these suggest, as you will see, that 50% is adequate, if not stellar. (The most authoritative is the last link, and they say, too, that 50% is ok.) The larger concerns I infer from your note is the utility of responses at low rates (we’ll let others worry for the moment about the implications of comparing results, as some chairs do, when the response rates differ significantly).
But our own work here at WSU with the College of Engineering suggests that the response bias may be less salient than one would presume.
We have not written this up yet, but here is a comparison of online versus paper done with the college of engineering at WSU. We have shared this with a work group from the American Evaluation Association (AEA) and are finding others who report the same phenomenon. The response rate online was about 51%, paper in class at about 71% (which is much lower than most people believe is the case for traditional paper-based, with the presumption that it runs closer to mid 90s). The samples are convenience samples based upon faculty preference for using paper or trying the online. The graph reflects 26 student evaluations randomly drawn from each of the three groups. If there is some kind of response bias, the picture here does not reveal it. We have been monitoring this as we move more and more online and remain interested in exploring the distinctions we may get (or not) when populations complete the instruments voluntarily, for extra credit, or when they are required to do so.
Nils Peterson, Theron Desrosier, Jayme Jacobson
CTLT has been thinking about portfolios for learning and their relationship to institutionally supported learning tools and course designs. This thinking has us moving away from the traditional LMS. It has also led to a recognition that grade books are QWERTY artifacts of Learning 1.0. In a recent Campus Technology interview Gary Brown introduced the term “harvesting gradebook” to describe the grade book that a faculty needs to work in these decentralized environments.
“Right now at WSU, one of the things we’re developing in collaboration with Microsoft is a ‘harvesting’ gradebook. So as an instructor in an environment like this, my gradebook for you as a student has links to all the different things that are required of you in order for me to credit you for completing the work in my class. But you may have worked up one of the assignments in Flickr, another in Google Groups, another in Picasa, and another in a wiki.”
This post will provide more definition and a potential implementation for this new kind of transformed grade book. It is the result of a conversation between Nils Peterson, Theron DesRosier and Jayme Jacobson diagrammed here.
Figure 1: White board used for drafting these ideas. Black ink is “traditional” model, Blue is a first variation, Red is a second variation.
The process begins with a set of criteria that is agreed by to be useful by a community and is adopted across an academic program. An example is WSU’s Critical Thinking Rubric. This rubric was developed by the processes of a “traditional” academic community. How the process changes as the community changes will be discussed below.
Instructors start the process by defining assignments for their classes and “registering” them with the program. Various metadata are associated with the assignment in the registration process. Registration is important because in the end the process we propose will be able to link up student work, assessment of the work, the assignment that prompted the work, and assessments of the assignment. More implications of this registration will be seen below.
The student works the assignment and produces a solution in any number of media and venues, which might include the student’s ePortfolio (we define ePortfolio broadly). The student combines their work with the program’s rubric (in a survey format). The rubric survey is administered to either a specifically selected list of reviewers or to an ad hoc group. (We have been experimenting with two mechanisms for doing this “combining.” One places the rubric survey on the page with the student’s work as a sidebar or footer (analogous to a Comment feature, or the “Was this helpful?” survey included in some online resources). This approach is public to anyone who can access the web page. The other strategy imbeds a link to the student’s work in a survey, it can be targeted to a specific reviewer. This example comes from the judging CTLT’s 2nd ePortfolio contest.
In either case the survey collects a score and qualitative feedback for the student’s work. We are imagining the survey engine is centrally hosted so that all the data is compiled into a single location and therefore is accessible to the academic program. Data can be organized by student, assignment, academic term, or course. A tool we are developing that can do this is called Skylight Matrix Survey System, which is rebranded as Flashlight Online 2.0 by the TLT Group. The important properties of Skylight for this application are the ability to render a rubric question type and the ability to have many survey instances (respondent pools) within one survey and both report instances individually and aggregate the data across some/all the instances.
Audiences for this data
The transformative aspects of this strategy arise from the multiple audiences for the resulting data. We have labeled these collections of data, and the capacities to present the data to audiences “assessment necklaces”
Figure 2: Diagram of rubric-based assessment. Learners, peers, and faculty are shown collecting data from rubric-based assessment of portfolios, then reflecting on and presenting the multiple data points (necklaces) in contexts important to them.
Students can review the data for self-reflection and can use the data as evidence in a learning portfolio. We are exploring ideas like Google’s Motion Chart gadget (aka Trednalyzer/Gapminder) to help visualize this data over time. They can also learn from giving rubric-based reviews to peers and by comparing themselves to aggregates of peer data.
Instructors can use the data (probably presented in the student’s course portfolio) for “grading” in a course. It’s worth noting that the Instructor’s assignments can be assessed with the same rubric, asking, “To what extent does this assignment advance each the goals of this rubric?” With the assignment rated, instructors can review the data across multiple students, assignments, and semesters for their own scholarship of teaching and learning (SoTL). Here the instructor can combine the rubric score of an assignment with the student performance on the assignment to improve the assignment. Instructors might also present this comparison data in a portfolio for more authentic teaching evaluations.
In this example the assignment might be rated by students or the instructor’s peers. Below, the rating of the assignment by wider communities will be explored.
Academic Programs can look across multiple courses and terms, for program-level learning outcomes and SoTL. They can also present the data in showcase portfolios used for recruiting students and faculty, funding and partners. This is where the collective registration of the assignment becomes important. The program can access the assignment in the context of the program, with an eye to coordinate assignments and courses to improve the coherence of the program outcomes.
The community, which might include accrediting bodies, employers and others, can use the data, as presented in portfolios by students, instructors, and the academic program, to reflect on, or give feedback to, the academic program. Over time, an important effect of this feedback should be to open dialogs that lead to changes in the rubric.
Variations on this model
The description above is still traditional in at least two important ways: the program (ie faculty) develop the rubric and the instructor decides the assignment. Variants are possible where outside interested parties participate in these activities.
First variation. WSU and University of Idaho run a joint program in Food Science. We have observed that the program enrolls a significant number of international students, from nations where food security is a pressing issue. We imagine that those nations view training food scientists as a national strategy for economic development.
We have imagined a model where the students (in conjunction with their sponsoring country), and interested NGOs, bring problem statements to the program and the program designs itself so that students are working on aspects of their problem while studying. The sponsors would also have an interest in the rubric, and students would be encouraged (required?) to maintain contacts with sponsors and NGOs and cultivate among them people to provide evaluations using the rubric.
The processes and activities described above would be similar, but the input from stakeholders would be more prominent than in the traditional university course. Review of the assignments, and decisions about the rubric, would be done within this wider community (two universities, national sponsors and NGOs). The review of assignments and the assessment of the relationship of assignments and learning products creates a very rich course evaluation, well beyond the satisfaction models presently used in traditional courses.
Second variation. This option opens the process up further and provides a model to implement Stephen Downes’ idea in Open Source Assessment. Downes says “were students given the opportunity to attempt the assessment, without the requirement that they sit through lectures or otherwise proprietary forms of learning, then they would create their own learning resources.”
In our idea of this model, the learner would come with the problem, or find a problem, and following Downes, learners would present aspects of their work to be evaluated with the program’s rubric, and the institution would credential the work based on its (and the community’s) judging of the problem/solution with the rubric. This sounds a lot like graduate education, the learner defines a problem of significance to a community and addresses that problem to the satisfaction of the community. In our proposed implementation, the ways that the community has access to the process are made more explicit.
In this variant, the decision about the rubric is an even broader community dialog and the assessment of the instructor (now mentor/learning coach) will be done by the community, both in terms of the skills demonstrated by students that the instructor mentored, and by the nature of the problems/approaches/solutions that were a result of the mentoring. The latter asks, is the instructor mentoring the student toward problems that are leading or lagging the thinking of the community?
Examples
For some sense of learning portfolios created by the processes above, consider these winners from CTLT’s 2007-08 ePortfolio contest.
The following two winners are examples of the second variant, where students were paired with a problem from a sponsor:
The Kayafungo Women’s Water Project documents the efforts of Engineers Without Borders at WSU (EWB@WSU) who partnered with the Student Movement for Real Change to provide clean water to 35,000 people in Kayafungo, Kenya.
The EEG Patient Monitoring Device portfolio follows the learning process of four MBA students who collaborated with faculty, the WSU Research Foundation, inventors, and engineers to develop a business plan for a wireless EEG patient monitoring device.
The next two are examples of the second variant — student defined problems assessed by the community. In the latter case, the student is using the work, both her activism in the community and her study-in-action as her dissertation:
The Grace Foundation started with a vision to create a non-profit organization that would assist poor and disenfranchised communities across Nigeria in four areas: Education, Health, Entrepreneurship and Advocacy. The author used the UN online volunteering program to form a team to develop a participatory model of development that addresses issues of poverty eradication from a holistic manner.
El Calaboz Portfolio chronicles the use of Internet and media strategies by the Lipan Apache Women’s Defense, a group that has grown in national and international prominence the last 75 days, from less than 10 people in August, to an e-organization of over 312 individuals currently working collectively. It now includes NGO leaders, tribal leaders, media experts, environmentalists, artists, and lawyers from the Center for Human Rights and Constitutional Law. It recently received official organization status at the UN.
The next steps in this work at WSU are to build worked examples of these software tools and to recruit faculty partners to collaborate in a small scale pilot implementation.
