Computer Information Science
2026-2027 Program Review

Computer Information Science (CIS) prepares students to put computers to work: to study computing for transfer, to keep computer systems and networks running and secure, to get work done with software, and to build websites and digital media. Employers in nearly every industry pay for these skills, whether in a computing career or in another field. As AI takes over more of the "how," CIS teaches the skills that last: understanding a problem, stating clearly what needs to be done, and checking that the result is right.

Our DI data show gaps for African American students in all six CIS subjects, for students below the poverty level in five, and for women and Hispanic/Latino students in some networking and security courses. The largest gaps are in gateway courses such as CISP 300, CISA 305/315, CISC 310 and CISS 310. Disproportionate impact describes a difference in outcomes; it does not identify a cause. We believe a major contributor is a difference in college readiness in reading, writing and math that students bring with them. In Sacramento County's 2025 grade 11 state tests, 46% of Black, 36% of Hispanic and 26% of White students scored below standard in writing, and only 28% of all students met the math standard. These skills strongly predict success in CIS. For example, students who can explain code in plain English are much more likely to write it.

The institutional floor. Several equity practices are built into how the college works, and CIS takes part in all of them. Under the LRCFT–LRCCD contract, the faculty evaluation process includes a required equity review. Every hiring committee includes a required equity representative, and the college president makes the final hiring decision. Through these processes the CIS faculty has become diverse: our ten faculty members come from seven racial and ethnic backgrounds. The gender gap, once wide, is narrowing, and three of the ten are now women.

Beyond the floor. Since 2021-22, every equity-focused objective in CIS's Annual Unit Plans has been driven by the Disproportionate Impact data set. We have taken the following actions.

STEM HomeBase collaboration (2021-22 to present). CIS has worked with the STEM HomeBase to give first-year students guidance specific to computing. The partnership is also a way to reach students more often and to refer students to reading, writing and reasoning support such as RAD, WAC, the Science Success Center and Women in STEM. [Example: what CIS faculty did with HomeBase, such as orientations, coaching referrals or workshops.] HomeBase check-in meetings kept departments informed and connected faculty with HomeBase specialists and counselors. The department has asked for those meetings to be reinstated.

Structured support-program participation and tracking (2023-24 to present). The department is building a way to match students with support programs that fit their needs. For example, it would refer a student who struggles with technical reading to RAD. The same mechanism would track both participation and any academic improvement. It would also help schedule CIS sections so they don't conflict with support-program times. [What exists so far, e.g. referral lists in Canvas, RAD presentations in classes ?.]

College readiness as a standing department topic. How to improve students' college readiness is a continuing topic at every CIS department meeting. Faculty share what they see in their classes and what has worked, and individual instructors adjust their teaching based on these discussions. [Example of an adjustment, if one is easy to name.]

Study skill and habit builder resources (2024-25 to present). Our conversations with students point to one driver of the DI gaps: many students were never taught the study and learning habits that the breadth and depth of CIS coursework demands. One-on-one help doesn't scale, and some students hesitate to seek it. So a CIS faculty member has written a series of openly available articles on study and learning habits, such as time management and note-taking, and some CIS instructors have built them into their course content. The same faculty member is developing these into a self-paced habit builder, tested with students in CIS classes (https://proftak.github.io/modules/0430/mdModule.html). [Number of courses or sections that use the articles.] Following the dean's 2025-26 review, the department also showed students how to build their own AI study assistants with Gemini Gems. The faculty-built AI tutors described below have since superseded that approach.

AI tutors and mentors for course content (2024 to present). Since 2024, a CIS faculty member has used LLM-based tutors and mentors in CISP 310 (Computer Architecture and Organization) and CISP 440 (Discrete Structures for Computer Science) to help students with subject-matter questions. They are available whenever students study, which helps students who work, can't attend office hours or hesitate to ask for help. Students have told the instructor that the approach helps them. The faculty member shares the results and the instructions given to the AI with colleagues, so other instructors can adapt them for their own courses.

Industry-partner presentations (2024-25 to present). Students in DI groups are often less aware of what employers expect. So the department and the student Computer Science Club bring in hiring managers and industry professionals to talk about hiring, employment and keeping a job. These events are shared with MESA, and recordings and summaries are planned for students who can't attend live. [Number of events or speakers in 2024-25 and 2025-26 ?.]

Capacity and access. To meet demand in cybersecurity, whose sections fill and waitlist, the department hired additional full-time faculty (completed 2025-26 ? confirm). It also revised the schedule so students can finish degrees and certificates in two years.

Lessons learned. From 2022-23 to 2023-24 the department explored HyFlex instruction, so that students could choose between online and in-person learning in the same section. We rescinded the objective in 2024-25 [reason: e.g. room technology, workload, or low student uptake ?].

Our strategies target skills rather than student groups, are open to every student, and do not lower the standards a passing grade certifies. A course's units cover the content of its course outline, so readiness support below college level cannot be added to the curriculum. It is not part of what instructors teach, and it is not included in the time the units assume. Our readiness support is therefore optional and takes place outside class.

Strategy 1: A course-specific readiness tool. The department will build an AI-based readiness tool for each pilot course, grounded in that course's curriculum and materials. It has two functions:

• Self-assessment: short, performance-based checks of the reading, writing, and math the course relies on, generated from the course's curriculum and reviewed by faculty.
• Guided concept tracing: a student pastes a paragraph or problem they find hard to understand. The tool explains how to parse it and asks questions to find what is missing. If the difficulty is an unfamiliar prerequisite concept, the tool follows that concept's own prerequisites, using a faculty-reviewed concept map of the course. It checks understanding at each step before returning to the original paragraph. The same approach applies to math, for example, subnetting back to binary, place value, and exponents.

The tool explains and gives hints; it does not complete assignments. Research shows that this kind of guardrail decides whether AI tutoring helps learning or harms it.

Strategy 2: Early detection and personal invitation. In the first weeks of the term, students in pilot courses take a short readiness check built from a fixed, faculty-reviewed item set. Instructors personally invite students whose results suggest a gap to use the readiness tool. They refer students whose needs go beyond the tool to college services: the Tutoring Center, Beacon, Reading Across the Disciplines (RAD), the Writing Center, and the ESL Center. In class, instructors will continue to emphasize the reading, writing, and math in each course outline, such as technical documentation and structured troubleshooting reports.

Implementation and evaluation. We will pilot the tool in two or three high-enrollment gateway courses with the largest gaps, then expand what works. With student consent, we will track tool use and the prerequisite concepts students most often lack. We will compare success, completion, and A/B rates by student group with our 2023-24 to 2025-26 DI baseline, and, where possible, look at success in the next course so improvement reflects learning rather than a change in the measure. This work extends our existing Annual Unit Plan objectives on the study skill/habit builder tool and on structured support-program participation and tracking.

• Faculty development time. Stipends or release time, including for adjunct faculty, to build and test the readiness tool for each pilot course. This includes writing each course's concept map, reviewing and fixing the readiness check items, and checking the tool's explanations for accuracy. This is development work outside teaching, and the CIS department lacks the resources to fund it.
• An approved AI platform and a privacy review. Access for students and faculty to a district-approved AI platform with educational data protection, such as Gemini for Education through the statewide California Community Colleges–Google partnership if the district adopts it. Also district review of the student-data and consent arrangements for tracking tool use.
• Institutional research support. Each term: section-level DI results for the pilot courses; a link between readiness-check results, tool use and course outcomes; next-course success rates; and, where available, results that account for prior preparation (for example, placement or prior GPA), to separate readiness from course effects.
• Learning Resource Center partnership. Designated contacts in RAD, the Writing Center, and the Tutoring Center for students whose needs go beyond the tool. Math tutoring for the math CIS uses (binary, subnetting, spreadsheet formulas) that is open to all students, since the Math Success Project serves only specific student populations.
• Professional development. Training for CIS faculty on using AI tutoring responsibly (guardrails, accuracy checking) and on interpreting the readiness-check results.