Sunday, January 25, 2015

Lycophyte and monilophyte hike

As I have been doing for the past few terms, the class made an immediate departure for the field. A brief stop at the transformer near the library provided a chance to cover Nostoc, mosses on the Ponapea ledermanniana provided a quick look at bryophyta. From there the class walked west to the trail.
Bryan Wichep and Miki Vivian Fritz out among the Lycopodiella cernua and Dicranopteris linearis, although in the photo one mostly sees polystachion grass and Merremia peltata.

Lerina Nena, Miki, and Lilina



At the top of descent the number of ferns has expanded. Davallia solida var. solida is on a tree just before this location. Around this area are now found Nephrolepis, Cyclosorus maemonensis, Microsorum scolopendria, Asplenium nidus, Sphaeropteris nigricans, Haploteris elongata, Huperzia phlegmaria, and Asplenium laserpitiifolium.

Lilina on the slope where an Asplenium species locally known at mahrekenleng is found. Tentatively either Asplenium polyodon or possibly A. pellucidum.

Possibly Cephalomanes atrovirens

Deeper in the dark, ferny depths of the forest

Some of the trees with Antrophyum callifolium Blume (indigenous) (Syn reticulatum G.Forst) had fallen down, but the ferns were still extant. 

Bryan Mwarike, Patty Mario near the Angiopteris evecta (not pictured)

The hike covered local names, some uses, and cases in which a particular plant has a cultural meaning. A lot to take in for the class this term.

Introductions and banana patch cleaning

Since the loss of the designated ethnobotanical garden area at the entrance to the college in the spring of 2014, I have shifted to using the plants across the full campus. I was able to move some plants to a new area to the south of the gym. Coupled with other plants that have grown out on campus, the campus is now effectively an ethnobotanical garden.

The first day of class I had brought along Ocimum tenuiflorum, Premna obtusifolia (topwuk), and a Premna obtusifolia variant locally distinguished as oahr. I had intended the Ocimum tenuiflorum as an easy plant for students to identify. When only one student could provide the local name, I realized that the class would be unusually weak in terms of local knowledge. As I result I prepared a campus flora handout in OpenDocument format with cross-reference between the Latin name and local names in four local languages.


The students can be seen clutching their flora sheets while standing under Senna alata.


Esmirelda Elias considers the baffling sea of indistinguishble greenery around her.


Lilina Etson knows her plants and recognized immediately that the Colocassia esculanta (jawa in her language, sawa in Pohnpeian) needed to be replanted. Elizabeth Augustine watches. Lilina cleaned the soft taro and passed them along to a Japanese student in the class.

The third day the class went to the banana patch to identify and clean up the bananas.


The above banana was reported to be a recent foreign introduction to Pohnpei and thus without a name.


Uhten rais.


Patty Mario hand cleans around the banana



Polystachion grass, Clidema hirta, and Costus speciosus are the principal invasives in the garden.


The instructor in the background

Herpelyn

Bryan Mwarike with an African tulip tree shoot


Rolling kick balls and linear relationships

I began the unit on linear velocity using the RipStik to generate a time versus distance relationship.


After I gather the data, the class copies the data. Although having a student ride the RipStik could shift the demonstration to a participation exercise, no student has had the ability to ride stably to date.


Laboratory two built off of the graphs I put on the board on Wednesday. The class sought to validate the predictions I had made on the white board.


A view down the walkway. In the afternoon I shifted the slower balls to the east where the slight slope offsets the frictional velocity losses.

The highest speed ball was an air ball.


Vancyleen recording data.


In the afternoon the launch point was moved to the east as seen above to get a steady slow speed ball roll.


As the speed increased, I backed the pitcher up the sidewalk to the west. Due to the geometry of the sidewalk and slight slope, this worked rather well, better than launching all balls from the exact same spot.


Tuesday, January 20, 2015

Social Media and Stimulant Survey

A convenience sample survey of social media and stimulant preferences was given to 69 students in three sections of MS 150 Statistics at the College of Micronesia-FSM.

FaceBook continued its dominant position among students surveyed, with 65 of 69 students having a membership in FaceBook. Two of the four non-FaceBook using students noted that they were not members of any social media web site. The other two listed membership in Google Plus.

In the spring 2014 only one student was using Instagram, by spring 2015 this number had risen to 15. The rise of Instagram is in part a product of the increasing penetration of smart phones into the student population. Thirty-eight (57%) of sixty-seven students reported accessing social media primarily via their cell phone. The increasing rates of social media access via cell phone provide impetus for the use of learning management systems that are mobile friendly. The statistics course uses Schoology which has both Android and iOS apps. The student interface to MyShark SIS should also be "mobile friendly" if at all possible.

Spring 2012 no student named Google+ in their responses. By fall 2012 thirty-two students listed Google+ membership. Since the initial uptake, usage had leveled off at around 40% . Spring 2015 membership jumped up to 70%. The dominant cell phone and pad platform here in Micronesia is Android, there may be a connection between increased use of Android platforms and Google+ membership.

FaceBook remains the social media place to be and to connect with students. As an anecdote, students who are accustomed to regularly managing their FaceBook presence appear to be better able to interact with learning management systems. FaceBook users are accustomed to keeping track of a username and password, navigating among screens with different functionalities, and making decisions on what to post. Students who do not as often use or do not use FaceBook are the students who come for help due to forgotten usernames and passwords, and difficulty understanding how to use learning management systems. This is all anecdotal, only impressions I get from working with students.

The survey also explored stimulant usage among the 69 students surveyed.


Although there are tantalizing indications of a possible drop in the percdenage use of betel nut, cigarette smoking may be on the rise among students. Despite the possible trend, usage of betel nut remains high with thirty (43%) of the 69 students reporting use of betel nut. Twenty-eight of the thirty report using the more carcinogenic combination of betel nut with tobacco. Cigarette tobacco is the leading tobacco choice among students. Cigarette tobacco was not intended for oral consumption, the additives do not get burned off and may also have a negative impact on oral health.


Saturday, January 10, 2015

Affective domain assessment of activities in ethnobotany course

The SC/SS 115 Ethnobotany class includes hikes into the forest, walks on campus, student presentations, field trips to off-campus locations, maintaining ethnobotanical collections, lectures, and hands-on activities. The course curriculum includes a dual focus both on the botanic diversity of local plants and on the ways in which these plants are used by Micronesians. The course does not have a botany course pre-requisite and is was designed to be a three credit non-lab science elective. Many of the students have no background in botany and some students have very limited knowledge of basic biologicial science concepts.


A single section of 24 students met Tuesdays and Thursdays for an hour and half each day fall 2014.
At the end of the term the students were asked to rate six types of activities from one most liked to six most disliked: field trips, gardening with machetes, hands on activities such as thatching, hikes and walks on and around campus, lectures, presentations. In retrospect this type of ranking evaluation appeared to be unfamiliar to the students. Some students applied the scale as if the scale were a Likert scale with which to rate each individual item. Other students understood that each number would be used only once.

As a result the actual score sums obtained do not necessarily have meaning. The low score sum, however, will still be the highest ranked activity while the high score sum will be the lowest rank activity. Despite the misuse of the ranking scale, the rank order does have meaning.

The numerically lowest sum of ranks score, a 38 for field trips, was an outlier on a box plot. Field trips were strongly favored over any other activity. At 56 hikes and walks on and around campus had the second lowest score sum making hikes and walks the second most favored activity. At the other end of the scale the highest sum was lectures. The class features only a couple of lectures during the sixteen week course, yet these few are apparently still too many.

Activity Sum of rank
Field trips 38
Hikes and walks on and around campus 56
Gardening with machetes 67
Presentations 68
Hands on activities such as thatching 71
Lectures 81

Separately students were asked to list their favorite thing about the class and their least favorite thing about the class. These two questions were open answer questions. Although the questions were open answer, there were common responses among the students. Field trips were a strong favorite. Secondarily presentations, hands-on activities, and hiking were favored.

Favorite Activities Freq Rel Freq
Off-campus field trips 10 42%
Presentations 5 21%
Hands-on activities/thatching 4 17%
Hiking 4 17%
Local food day 3 13%
Walking and identifying plants 3 13%
Everything 1 4%

Once again lecture was least favored. Presentations were also disfavored. The students who disliked presentations expressed being shy in front of people, or of their presentation not going well. Of some interest to me was that the work of maintaining the ethnobotanical collection, outdoor work with machetes, was only chosen as least favored by three students. For the remaining 21 this was not a least favored activity. Cleaning around the plants that comprise the campus collection is grounds keeping level work, yet the students would apparently prefer that to sitting in a lecture.

Least Favorite Activities Freq Rel Freq
Lecture 9 38%
Presentations 6 25%
Maintaining ethnobotanical gardens 3 13%
Walking/hiking in rain and mud 3 13%
None 3 13%

The survey results generally reflect my own sense of what the students enjoy and what they do not enjoy. The results tend to confirm my own sense of how the students are reacting to the class, and reassured me that the hot, sweaty work of maintaining the collection is not strongly disfavored.

General Education Laboratory Science Common Assignment Assessment and Institutional Memory

Institutional memory is always a challenge at any institution. Despite the best laid plans, acres of file cabinets, and buildings stuffed with servers and RAID arrays, institutional memory still often comes down to some old codger who can tell the story. This old codger, however, has difficulty remembering the specific details of days, dates, and times. My blog is a proxy for my faulty memory, a place I can write stuff down and then find it again using Google and a search restricted to site:danaleeling.blogspot.com. Thus my blog is really only notes to future self, a part of my personal memory, an extension of my mind.


Over the break I have been reading The Innovators and was reminded of the early tension between those who sought to create computers that would be functional equivalents of humans: intelligent agents, robots perhaps, which/who would function independently of people and those who sought a human/computer symbiosis where the computer extended the capabilities of humans. My blog is an extension of my memory systems, a computer as a symbiont.

This blog is such a note to myself, an update on the general education laboratory science assessment over the past five years. I would note that this history does not reflect my own ongoing assessment in physical science since 2007. Course level assessment in physical science in recorded up through 2009 via links on an assessment page and after 2009 in a thread within this same blog.

General education laboratory science program level assessment via a common assignment was begun with work towards adopting a rubric by which to assess a common assignment. This work was reported on in an article in early 2012. A retrospective overview was completed in August 2012 along with recommendations on next steps. Inter-year assessor issues were a cause for concern at that time.

Fall 2012 some laboratory reports were submitted. At that time the general education assessment was being overseen by a full time faculty member as an additional duty. The faculty member was effectively operating without a budget and could not stipend faculty to do the reading and rating work that had to be done. The faculty member also did not have any sort of organizational authority over faculty across the system, complicating efforts to get the common  assignment submitted from all sites. Situations such as this helped drive the redevelopment of an existing unfilled assessment coordinator's position and the elevation of that position in the academic hierarchy of the college.

During the fall of 2013 instructors involved in the general education laboratory science assessment discussed using a different rubric. An instructor at the Chuuk campus shared a rubric in use on their campus, I also shared a 50 point rubric I use in my class.

An instructor at Pohnpei site expressed a preference for the 50 point rubric I had shared. Follow-up emails from the other instructors indicated a willingness to use the rubric I had shared, although the instructor in Chuuk expressed concern that the 50 point rubric was complex and difficult to use.

While the original idea was to gather laboratory reports and have an independent team read the lab reports and score them against the 50 point rubric, this did not occur.

Although the instructor in Chuuk had been the only instructor to express reservations about the 50 point rubric, the instructor in Chuuk would be the only instructor to report results against the 50 point rubric in a January 2014 email. The analysis was different than the one's I had been doing with the rubric, so I re-analyzed my laboratory reports using the criterion selected by the instructor in Chuuk. This led to the following data:

Chuuk campus:
Common assessment assignment (laboratory report) scores for SC117 Tropical Pacific Islands Ecology
Number of students: 26
7 students scored 80% or above on this assessment
5 students scored above 70%.
The other 14 either scored below 70% or did not turn in this lab.

National campus:
SC 130 Physcial science laboratory analyzed: 072 linear relationship between minutes of longitude and meters
Original number of students registered in the course at term start: 30
Number of students registered in course at time lab was due: 27
Greater than or equal to 80%: 13
Greater than or equal to 70%: 4
Below 70%: 4
Missing (not turned in): 6
Withdrawals from the course prior to laboratory being due: 3

At that time I asked the other instructors to perform the same analysis but never received a response. I felt that I had responsibility for obtaining results but no actual authority. I was also concerned that the data above was too aggregated to provide specific information for improving courses. Breaking out scores by the ten categories on the 50 point rubric might yield more actionable information.

As far as I know, laboratory reports were not gathered fall 2014 as a common assessment. Course level assessment and the impact on program learning outcomes continues to be reported.

One request by faculty from prior terms was to find a way to physically bring together all of the general education science laboratory instructors at some point, system-wide, to discuss what the data meant and which way general education science program level assessment should go in the next set of cycles. Although expensive, physical face-to-face meetings still cannot be replaced by electronic options. At some point all of the general education science laboratory instructors need to sit around a common table and hash out what we know, what we want to know, and how we will accomplish that mission going forward.

Friday, December 12, 2014

Assessing learning in introductory statistics

MS 150 Statistics has for the past three years utilized a modified curriculum based on a proposed outline. The three course level student learning outcomes for MS 150 Statistics are:

  1. Perform basic statistical calculations for a single variable up to and including graphical analysis, confidence intervals, hypothesis testing against an expected value, and testing two samples for a difference of means.
  2. Perform basic statistical calculations for paired correlated variables.
  3. Engage in data exploration and analysis using appropriate statistical techniques including numeric calculations, graphical approaches, and tests.

The first two outcomes involve basic calculation capabilities of the students and are assessed via an item analysis of the final examination. 76 students in three sections took the final examination.

Measuring flight distance towards calculating a 95% confidence interval for the flight distances. Students build and throw their own aircraft, then the calculation shows that the confidence interval captures a previously published population mean distance.
Twenty-one questions on the final examination required the students to perform basic statistical calculations on a small sample. Based on the item analysis, 80.2% of the items were answered correctly by the students. In general basic single variable statistical calcuations are an area of strength for the students.

Performance on the second student learning outcome was measured by eight questions on the final examination. Student performance on this section was lower at 68.2%. This section has historically been weaker than the basic single variable statistics section.

The third student learning outcome, open data analysis, was assessed using a simple rubric that looked at whether a student made an appropriate analysis with correct answers to questions posed in the problem and the level of statistical support for those answers. The results for the 76 students are reported in the following table and graphically in the chart.


n RF Performance
10 0.132 An appropriate analysis with optimal statistical support for that analysis
20 0.263 An appropriate analysis with reasonable statistical support for that analysis
9 0.118 An appropriate analysis with minimal statistical support for that analysis
18 0.237 Specific questions are answered correctly but without statistical support
17 0.224 An inappropriate analysis with incorrect answers to posed questions
2 0.026 A statistical analysis that should have led to correct answers to posed questions, but those questions were left unanswered.




51% of the students answered correctly with varying levels of appropriate statistical support. Another 24% were able to obtain correct answers but did not cite supporting statistical evidence. Just under a quarter of the students, 22%, answered incorrectly. Some of the incorrect answers included the appropriate statistical analysis, but the wrong conclusion was drawn from the results. Other incorrect answers were when the student left the section blank. Observations during the examination did not indicate that students "ran out of time" to work this section, but rather simply did not know what to do with the open data exploration.

Overall performance on this section on a point basis was weak with a 36.5% average on this material - only optimal answers received full credit in points.

Data rarely comes wrapped up with nice neat specific statistical calculation questions such as "What is the mean of this data?" Data comes with general questions and the data analyst has to choose the appropriate tools for the analysis. The open data exploration seeks to probe the students' ability to handle data in the "wild."

The three sections corresponding to the three student learning outcomes have been measured since the fall of 2012. The following chart provides some historical perspective on these values.


The chart shows student success rate performance against the three proposed outcomes for the past five terms. The uppermost, yellow, circles are performance levels on single variable basic statistical calculations - course learning outcome number one. The middle, blue, circles are performance levels on two variable linear regression calculations - course learning outcome number two. The lowermost, orange, circles are the performance on the open data exploration. Note that the variation in performance on the open data exploration is due in part to differences in the marking schemes term-on-term. To some extent the open data exploration is not comparable across terms due to differences in the marking schemes. The marking scheme used fall 2014 is similar to that used spring 2014 and those performances can be compared. 

Overall students prove quite capable, by term end, of making specific statistical calculations when told what calculation to make. When given data and questions that do not explain what statistics should be calculated, student performance is weaker. This addition of open data exploration to the course was stimulated by the American Statistical Association's Guidelines for Assessment and Instruction in Statistics Education. In the full report the ASA recommends to "give students plenty of practice with choosing appropriate questions and techniques, rather than telling them which technique
to use and merely having them implement it." The ASA also calls on statistics instructors to move from using naked or realistic data to using real data. My data remains in the realistic realm more than in the real realm, the exercise does require the student to choose the appropriate techniques. MS 150 Statistics continues to seek to implement best practices in the field of statistical education.