The DoIT Technology Support Center is looking to hire additional student consultants to work during the summer semester and beyond. Please see our job description below and visit our job posting on UMBCWorks to apply. (Search for TSC Student Consultant.) The application will be open until April 29, 2022.

We're specifically looking for students available to work during the following hours:

• Mondays 4 p.m. - 8 p.m.
• Tuesdays 8 a.m. - 10 a.m.
• Fridays  8 a.m. - 12 noon, and 3 p.m. - 5 p.m.

TSC Student Consultant Job Description

Technology Support Center (TSC) student consultants are the first points of technical contact for the UMBC community and those who do well often go on to work in other areas of the Division of Information Technology (DoIT). Minimally, consultants are expected to provide initial triage and support for a wide range of information technologies including accounts, hardware, instructional technologies, networking, software, telecommunications, and basic web development.

Responsibilities include, but are not limited to the following:

• Provide technical assistance to users by phone, online and in person;
• Answer TSC phones in a professional and courteous manner;
• Create, update and resolve tickets in the Request Tracker (RT) ticketing system;
• Create, update, suggest and recommend relevant FAQ articles to users (www.umbc.edu/faq);
• Provide direct assistance to users on technical issues they cannot easily resolve on their own;
• Respond to all work-related communications in a timely manner;
• Escalate urgent problems to the Full-Time staff, as appropriate.

Required Skills and Experience

• Demonstrated ability to effectively communicate by phone or in person.
• Demonstrated writing ability.
• Demonstrated ability to achieve successful outcomes in handling difficult situations and customers.
• Demonstrated analytical and troubleshooting skills.
• Ability & willingness to learn coupled with a clear understanding of one’s technical abilities, so as to triage and escalate an issue that leads to an effective solution for the user.

The Data Science Team recently released its set of predictions for the 6-year graduation probabilities of the 2020 Freshman cohort. Below is a snapshot of the current cohort's predictions, in blue, with the 2019 Freshman cohort's predictions, in red. 

The vertical axis represents the end of 2nd semester prediction probability; the horizontal axis is the end of the 3rd semester prediction. 

The black diagonal line indicates the point on the graph at which the second semester and third semester predictions are equal. If a dot, representing an individual student, rests on the line there was no change in the student's predicted probability of graduating between their second and third semester. Points above the line mean a student's predicted 6-year graduation probability decreased from semester 2 to semester 3. 

The group of students above the line, particularly in the Moderate and Low probability color bands, are of concern as their predicted probabilities of graduation have declined over their last two completed semesters. 

The 2020 Freshman cohort was the first freshman class during COVID and one of the concerns is the effect COVID may have on student performance in later semesters. From the predictions chart there seems to be reasons for optimism. Looking at the dispersion of points, there is a greater concentration of students in the High and Very High likelihood buckets compared to the 2019 cohort, as shown by the clustering of blue dots in the upper right corner of the chart. 

However, some concern may remain. Students whose predictions fall in the Low and Moderate buckets have experienced sharper declines in predicted likelihood to graduate than students from the 2019 cohort (because the blue dots, 2020 cohort, are much further above the diagonal line than the red dots, 2019 cohort). 

The diagram we've shown in this post is produced by a tool developed by the Data Science Team.  The tool allows staff with broad student success responsibilities to identify groups of students who may need additional support to achieve their goals.  The tool is available at analytics.umbc.edu in the student success section.  Access to it requires permission.  

(NOTE: Please join us Friday, March 18th from 12-1pm, for the fourth in our series of our Spring 2022 Data Science Workshops. This predictive model will be the topic of the session. Go to this link - https://my3.my.umbc.edu/groups/instructional-technology/events/101281 to register)

Len Mancini

Data Scientist and Statistical Specialist