Splash

Welcome!

Here I have been tasked to present my work at UC Merced for evaluation by bosses and coworkers. Most of my time as a lecturer is spent teaching data science courses—mainly for life science undergraduates. This presentation is done as a dashboard, which is a tool used by industry professionals in the data science field to communicate information and results to colleagues and clients.

The evaluation period is from July 2016 to June 2019. In addition to an overview of the numerous courses that I taught (as instructor of record) at UC Merced, I hope to give the reader a view of various other efforts and accomplishments of mine.

To navigate around this app, click on the tabs above (such as “Bio 18”). I hope that this dashboard allows for expedient review. Please do not hesitate to ask me for clarifications or more material about my case.

Sincerely,

Derek Sollberger

Continuing Lecturer in Applied Mathematics

Contents

My portfolio includes

• certifications for research ethics and from online coursework in data science
• course materials (syllabi, exams, and other class materials)
• end-of-semester evaluations from samples of students
• letters of recommendation volunteered by colleagues and former students

Students Taught

1478

Weighted Teacher Rating

Teaching Assistants Supervised

36

Winner of the Margo F. Souza San Joaquin Valley Mentor of the Year Award

2018

Dollars in Grant Money Awarded

1500

An Overview of My Teaching

After being promoted to continuing lecturer in 2017, I was able to reduce my classroom responsibilities to just the university (rather than multiple colleges). I have been fortunate to have several semesters being the sole instructor of record for the Math 15 and Bio 18 courses (the latter was previously labeled “Math 18”). These are the first- and second-semester data science courses at UC Merced, and they are mainly taken by the biology majors. Each semester, I have strived to continue to innovate the learning environment so that my students learn the cutting-edge technology in the data science field and are inspired to continue learning after the course.

With help graduate students leading the discussion sections and labs, we help the students through real-world data and examples, and then we guide the students through a semester project with Bio 18. This project allows the students to investigate a topic of their choosing and carry out several research steps in exploratory data analysis, which is capped with a poster session.

For my own aptitude, I have continued to take online courses in data science. In particular, I have taken 97 online courses through Data Camp, who collected state-of-the-art lessons taught well with many coding exercises and by some of the preeminent data scientists in the industry and academia. Each of those courses was about 5 hours in length; thus this commitment was about 500 hours in length. For guidance, I completed all seven of their “tracks” to indicate my training as a data analyst and a data scientist.

While I come to the bioinformatics courses as a computer programmer and a person that majored in applied mathematics, I do not want to take my presence in the biological sciences for granted. I have read the entire cell biology textbook (the one used for Bio 2 and Bio 110) for a foundation of knowledge, and I occasionally look into the current trends of research in single-cell RNA.

In the Spring 2019 semester, I was blessed with the opportunity to take on two upper-division courses: Bio 184 in the biological sciences department and Math 181 in the applied mathematics department. This past semester was the first time that I was the instructor of record for both of those courses and the first time that I was instructor of record for upper-division courses. It was quite and adventure, and I have outlined those courses in the tabs here in this dashboard.

Over the past 2 academic years, I have submitted 4 proposals for Spark courses. The Spark courses are specifically for incoming students to teach toward a research topic in a small classroom setting. My latest proposal on Sports Analytics, which leverages my expertise in data science and hobby of baseball statistics, was accepted and I look forward to teaching that course along with Bio 18 and Bio 184 in future semesters.

On Philosophy and Teaching Style

My data science courses employ a live coding style where I present a data analysis in front of the audience and guide the students and they code along with me at key steps (having usually provided a skeleton template for the sake of time). This style of instruction is done at data science bootcamps such as Data Carpentry. Doctors Sabah Ul-Hasan and Katie Coburn brought Data Carpentry sessions to UC Merced, and that is the modern teaching style that I emulate in my courses. This style allows me to express the how and why for each line of code and impart good coding practices with the students.

Math 15

• Broad lectures (that met only once per week) to give an overview of the modern data science field
• Clear laboratory assignments where students know which skills are to be demonstrated each week
• Students are assessed on number sense (“Which results are good or bad, large or small?”) so that they can interpret results from statistics calculations and tests.

Bio 18

• The lecture sessions are split: the first half is done with pencil and paper to do classical statistics examples and introduce concepts, and in the second half I have the students follow along as we type computer code to handle realistic examples in modern settings.
• With the plethora of goals I have for the students—including arithmetic calculations, computer programming results, data visualization, definition checks, concept checks, and number sense—there are several low-stakes assignments where I ask students to be mindful of a variety of responsibilities.
• The semester project brings the wide array of material together into a result that students can gladly add to their e-portfolio.

Bio 184

• Lectures are split: most of the time spent with simultaneous coding to introduce computer programming syntax and good programming practices. The rest of the time are supervised coding sessions where students will encounter computer programming hurdles and develop the habit of how to best seek direct assistance.
• Students are advised to draft and outline their computer code before taking on complicated tasks.
• The mid-semester and final project highlight computer programming and data science skills that are highly sought in industry.

However, relatively new literature endorses flipped classes and active learning techniques. Starting in the Fall 2019 semester, I will try out these ideas in my data science courses to see if they are even more engaging and inspiring.

Future Goals

General

• Employ an active learning environment with a TEAL room (Technology Enhanced and Learning) at UC Merced

Near future

• useR!2019 (Toulouse, France): once again attending the largest annual conference of R programmers where I can keep up to date with the data science industry and discuss pedagogy with fellow data science instructors
• BioQuest QUBES (Williamsburg, VA): week-long immersion with biology researchers and professors to develop engaging classroom materials and infuse data science techniques
• Active Learning Workshop (SF State Univ): 3-day workshop to learn about the newest ideas in active learning pedagogy and flipped classrooms
• Science in the Classroom (UC Merced): one-day workshop about how to modernize science instruction

Bio 18

• Assign homework through RStudio Cloud. The cloud computing will allow students to start computer programming without classic hurdles such as downloading software and adding code libraries.
• Build a code library to streamline probability calculations. This will allow focus on the abstract math concepts rather than coding syntax.

Bio 184

• Add a module that will introduce the current research trend of single-cell RNA studies to this course which already features DNA analysis.
• Assign homework through a Docker environment which will allow students to start computer programming without classic hurdles such as downloading software and adding code libraries.

Spark

• Assemble repository of sports databases
• Collect examples from job applications in some sports franchises

Versatility

I feel comfortable teaching any of the following courses at UC Merced.

• Bio 18, 175, 180, 184
• Engineering 155
• Math 5, 11, 12, 15, 21, 22, 23, 24, 32, 50, 131, 181

Timeline

Here is a searchable timeline of the courses that I taught during the 3-year evaluation period. The information I have collected here includes the number of students that filled out instructor evaluations. Those numbers were then used to compute the weighted average teaching rating. I was the instructor of record for each of these courses except for my brief stint as a teaching assistant for Math 32.

Weighted Teacher Rating

Semesters and Innovation

Summer 2017

• Experimentally, I ran the course with the homework and 1/3 of the exam questions in multiple-choice form to assess students’ knowledge of graphs and functions.

Portfolio Contents

In the submitted portfolio, I have included one each of

• exam
• the new, online homework questions
• syllabus
• worksheet

Homework Example

Semesters and Innovations

Summer 2016

• Updated the course to contemporary concerns in the data science industry

Fall 2016

• Added clicker usage for a more interactive learning environment

Spring 2017

• Streamlined transition from homework questions to final exam and wrote code to automate Scantron processing

Summer 2017

• Attended my first R programmers conference

Fall 2017

• Modernized lecture slides into dashboards (like this one) so that students can refer to material faster

Spring 2018

• Rearranged labs into dashboards (like this one) so that students can view the entire assignment faster

Fall 2018

• Remade online homework assignments in Shiny apps to randomize data and numbers for each student

Portfolio Contents

In the submitted portfolio, I have included one each of

• lab assignment
• lecture slides
• final exam
• online homework (best viewed here)
• syllabus

Lecture Example

Semesters and Innovations

Summer 2016

• Nearly all examples presented in class or worksheets are real-world examples

Fall 2017

• Started having students do data analysis semester projects—capped with a poster presentation

Spring 2018

• Spaced out project benchmarks to aid students in the investigation process

Fall 2018

• Started using the students’ projects to inspire final-exam questions

Spring 2019

• Investigated how to incorporate anatomy diagrams in computer programming practice

Portfolio Contents

In the submitted portfolio, I have included one or two each of

• homework programming assignment
• final exam
• project poster
• syllabus
• worksheet

Lecture Example

Semester and Innovations

Spring 2019

• all notes and assignments were done in Jupyter Notebook (like industry professionals)
• added medical imagining as a project

Portfolio Contents

In the submitted portfolio, I have included

• Jupyter homework prompts
• project prompts
• student project submissions
• syllabus

Medical Imaging

Semester and Innovations

Spring 2019

• wrote a “textbook” of class notes to guide math and computer science students through a thorough introduction to stochastic processes
• developed animations to illuminate complex topics

Portfolio Contents

In the submitted portfolio, I have included

• final exam
• syllabus
• textbook that I assembled

Animation Example

Past Project

Active Learning Techniques of Large Lecture Courses

• statistics consult
• Biology 1

Here I gathered the grades from multiple semesters of Biology 1 and used statistical tests to compare the scores from before and after active-learning and flipped classroom techniques were applied by the team teaching trio of Kamal Dulai, Chris Amemiya, and Nester Oviedo.

The results were presented by Kamal Dulai at the SABER-West pedagogy conference in January 2019.

Current Project

Active Learning Techniques of Discussion Sections

• statistics consult
• Chemistry 1

Here I gathered the grades from multiple semesters of Biology 1 and used statistical tests to compare the scores from before and after active-learning styles were applied to the discussion sections of the Chemistry 1 course by lecturer Mark Vidensek.

The research team consists of Mark Vidensek, Dusty Ventura, Kamal Dulai, and myself. We are currently seeking IRB approval to continue this project with more thorough surveys of volunteer students. In addition to comparing quiz and exam scores, we plan to track student confidence levels and student learning outcome attainment over the course of a semester.

Conferences and Workshops Attended

7

Conferences and Workshops Attended

Summer 2017

• useR!2017 (Brussels, Belgium): learned more about markdown (literate programming) and brought back that coding style to my courses

Spring 2018

• ESRI DevCon (Palm Springs, CA): connected with industry developers of geographic information systems (GIS) to discuss how we currently handle spatial data in the data science field.
• OpenStax Open House (Houston, TX): in OpenStax’s first ever conference event, instructors from several American colleges worked in teams to draft open source exercises for OpenStax’s goal to provide free textbooks. This collaboration led to me being awarded $1500 through the Zero-Cost Course Materials grant (ZCCM, through UCM library)!

Summer 2018

• UndocuAlly (UC Merced): two-day session to learn about undocumented immigrants and their current place in academia.
• useR!2018 (Brisbane, Australia): met with fellow data science instructors from around the world to discuss priorities in the material that we present to students, and I drafted (later implemented) ideas to randomize homework assignments to discourage academic dishonesty.

Spring 2019

• Pi Kapp College (Dallas, TX): attended training sessions for the Pi Kappa Phi fraternity to learn all of the present issues with fraternities in America today.
• Merced Math Teacher’s Circle (UC Merced): high school and university math teachers meet to try out classroom activities that encourage inquiry-based learning

General Education Executive Committee (GEEC)

• Unit-18 Lecturer Representative (Non-MWP instructors)
• Voting member
• Co-Curricular Badges Subcommittee

Starting in the Spring of 2019, the GEEC has met for over 3 hours per month to handle the growth of the general education program at UC Merced. The challenge is to build a program to meet the needs of a rapidly growing research institution while balancing the concerns of various groups around campus.

Alumni Association

• Board Member
• Career and Professional Development Committee
• Nominations Committee

I am currently serving a two-year term on the UC Merced Alumni Association. In addition to coordinating many activities for the Homecoming and Bobcat Day showcase days, we carry out scholarship endowments for current students and engage with alumni for donations and professional development.

Engineering Service Learning (ESL)

• R Instructor
• Tidyverse Instructor

Starting in the Spring of 2015, I have volunteered to give 2 seminars per semester for ESL. My workshops introduce students (mostly undergraduates, sometimes graduate students) to the R programming abilities and community. Each semester, I have updated my workshops to be more engaging, more sequential, and to include the most up-to-date tools in the R programming data science industry.

Undergraduate Research Opportunities Consortium (UROC)

• Python Instructor
• R Instructor

In June of 2019, I filled in as the computer programming instructor for UROC, and I started to develop all-day workshops for budding research interns at UC Merced.

DatASci

• faculty adviser
• founding member

DatASci was a graduate-student club that reached out to graduate students across nearly every discipline at UC Merced. Our goals included providing a smooth introduction to data science skills and project consulting. Sabah Ul-Hasan also brought in guest speakers from the Unconscious Bias Project.

These efforts nominated and awarded me the 2018 Margo F Souza San Joaquin Valley Mentor of the Year award!

In 2018, I partnered with DataCamp and a graduate-student group of STEM researchers so that we could outsource data science instruction to DataCamp at zero cost for the graduate students. I held weekly sessions for graduate students who wished to do these training exercises in person.

Pi Kappa Phi

• Chapter Adviser
• Alumni Initiate

Since becoming chapter adviser in Spring 2017 and becoming a fraternity brother in Fall 2018, I have guided the members of our Pi Kappa Phi chapter through issues of morality, activities on campus, and graduating to a post-college life.

Other Affiliations

Board Game Club

• Faculty Adviser
• this club provides undergrads with recreation each Thursday evening

Circle K

• Faculty Adviser
• this chapter of Kiwanis International strives for two community service projects per month

Game Development Club

• Faculty Adviser
• these programmers have aggressively planned game projects and started to build prototypes

League of Legends Club

• Faculty Adviser
• this club provides recreation to undergraduates every Friday evening

STEAM Center

• guest speaker
• I gave and will continue to give “What do I do as a data scientist?” talks to children (between 9 and 11 years of age) at the STEAM Center specialized school in the city of Merced.

Letters of Recommendation Written

27

Letters of Recommendation

With aid from letters of recommendation that I have written, my students and teaching assistants have applied and/or proceeded to jobs and opportunities such as

• graduate-student teaching awards
• high school instruction
• medical school
• ophthalmology school
• pharmacy school
• UROC

Data Consulting

In addition to the research projects listed before, I continue to present myself as a data science and statistics consult for those around me, and I have been assisting about two graduate students per semester with their calculations and modeling.

Reading List

In order to continue growing as a lecturer, my near-future reading list includes

• for active-learning activities: Teaching Statistics: A Bag of Tricks by Andrew Gelman and Deborah Nolan
• for statistics history: The History of Statistics by Steven Stiegler
• for statistics holistically: The Seven Pillars of Statistics by Steven Stiegler
• for sports analysis: Analyzing Baseball with R by Max Marchi and Jim Albert

Disclaimer

My plans had included finishing 100 online courses with DataCamp and renewing our free access for graduate-student researchers by now. However, I have since severed our working relationship with DataCamp in solidarity with many data scientists after fierce allegations of sexual harassment and a year-long cover up were released in April of 2019.

About

This app was created by Derek Sollberger in June 2019 with

• RStudio integrated development environment (IDE)
• FlexDashboard code library (made by Dr Elaine McVey)