CIS 1400 Introduction to Computer Science I - Lecture Notes (Spring 2025)

Course Overview

  • This lecture is an overview of CIST 1400 (Introduction to Computer Science I) taught by Dr. Robert Fulkerson at UNO. It covers the course purpose, pedagogy, textbook, weekly workload, and key announcements for the semester (Spring 2025 in the transcript).

Instructional Team

  • Instructor: Dr. Robert (Bob) Fulberson. Has been at UNO for 30 years.
  • Roles: teaches and coordinates CIS 1300 (intro to web development), CIS 1400 (this course), and CS 20/50 (intermediate software/web development).
  • Pronouns: EM.
  • Support staff: undergraduate Learning Assistants (ULAs), a grader for the semester, Graduate Assistants (GAs), and other university-wide collaborators. The course is developed over years by a multidisciplinary team across colleges, departments, and the CS department to serve diverse curricula (CS data structures, software development, software engineering, game programming, etc.).
  • Contact information: available in Canvas under the course’s modules (How to get help, instructors page, graduate and undergraduate learning systems page).

Course Purpose and Core Focus

  • Primary aim: problem solving using the computer as a tool; introductory CS with emphasis on computational thinking and programming.
  • Language: Python, chosen for accessibility to both novices and those with some programming background.
  • Two learning strands:
    • Computational thinking: how to approach problems and plan steps (e.g., drawing an image on screen).
    • Specific programming language syntax and constructs in Python.
  • Five learning outcomes guide all course activities.

Five Learning Outcomes (Key Concepts)

1) Concepts of the software development process: how to write a basic program in CS1 (with broader implications for software engineering in advanced study).
2) Correct usage of data types: selecting appropriate data types, data conversions between types, and implications of data representation in programs.
3) Correct usage of control structures: selection (decision-making) and repetition (loops); relation to high-level concepts across languages (Python, C, Java, etc.).
4) Correct usage of functions and parameters: modularizing code into reusable pieces.
5) Correct usage of linear collections of data: handling large data, data collection, efficiency, and management.

  • Note: High-level concepts apply to any language; implementation differences exist between Python and other languages.

Why Computer Science? Big Data and Real-World Relevance

  • Personal anecdote connects CS to broad data processing:
    • DNA testing (23andMe) and ancestry data illustrate how large datasets are processed at scale.
    • 23andMe genotype customers: ~14,000,00014{,}000{,}000; Ancestry genotype customers: ~25,000,00025{,}000{,}000.
    • Reference samples (for ancestry classification) have grown from 3{,}000 (2017) to 68{,}000 (2023) for Ancestry, enabling more precise inferences.
    • Differences in percentages between companies reflect varying reference datasets, not necessarily accuracy differences: more data can refine estimates.
  • Big data as a career area: data processing, machine learning, AI, software engineering, networking, etc.
  • Emphasis: computation is pervasive across disciplines (geology, biology, geography, library science, etc.).

Course Logistics: Online/Hybrid Structure

  • Online CS is challenging due to non-co-located interactions; the course is designed to create a learning community with scheduled touchpoints.
  • Online STEM courses historically have varied DFW (drop/fail/withdraw) rates; UNO reports improved outcomes due to structured pedagogy:
    • Online CS major enrollment historically around ~15%; at UNO, online courses see ~40–50% majors in the online CS context.
    • Research indicates online students often report low self-efficacy and face distractions; success predictors include time management, timely submission, and active engagement with coursework.
    • Submitting assignments early and using LMS (Canvas) effectively are associated with success.
  • UNO’s approach aims to reduce DFW to ~15%15\% for online students (historical figure cited for online courses). This is substantially better than some reported online CS one-class DFW rates (historically 50−60%50-60\% in certain studies).
  • The course design includes weekly rhythm and explicit expectations to help manage time and maintain momentum.

Course Structure and Grading (Five Categories)

  • There are five grading categories; three are textbook-based.
    • Participation activities (in-textbook): 10% of grade.
    • Challenge activities: 15% of grade.
    • Many Small Programs (MSPs): 20% of grade.
    • Lab and weekly progress checks: 25% of grade.
    • Quizzes (five quizzes): 30% of grade.
  • Formula for overall grade (weighted):
    G=0.10⋅P<em>part+0.15⋅P</em>chal+0.20⋅P<em>MSP+0.25⋅P</em>lab+0.30⋅PquizG = 0.10 \cdot P<em>{part} + 0.15 \cdot P</em>{chal} + 0.20 \cdot P<em>{MSP} + 0.25 \cdot P</em>{lab} + 0.30 \cdot P_{quiz}
    where each $P$ is the earned percentage in that category.
  • Recitations and labs are hybrid components:
    • Recitations are 90-minute small-group sessions (16 sections available) led by GAs or ULAs.
    • Recitations include a brief material highlight, time for questions, and a guided programming activity; there is no new lecture in recitation.

Textbook and Materials: ZyBooks First Day Access

  • Textbook: ZyBooks (online) with First Day Access; sign-up is available via the Week 0/Week 1 Canvas module.
  • Cost: $90 total; charged to the student account (First Day Access) and appears on the student bill; no paper copy; electronic-only access.
  • Sign-up flow: sign up during first week; the system may show $0 at signup, but it will be billed to the account.
  • Textbook structure:
    • Three activity types: Participation activities, Challenge activities, MSP activities (Many Small Programs).
    • Each section includes a short instructor-made video; videos are complementary—not a replacement for reading.
  • Activities in ZyBooks:
    • Participation activities: animations, MCQ, fill-in-the-blank, ordering items; problems cannot be completely wrong; grading is based on completion (10% of grade).
    • Challenge activities: multi-step problems; can be incorrect; typically require multiple levels to demonstrate understanding; contribute 15% of grade.
    • MSPs (Many Small Programs): 4–8 MSPs per week; each MSP yields points; typically 10 points per MSP with a total around 50 points; completion threshold for a perfect course grade is 95% of MSPs to reach 100% in MSP component.
    • Feedback and learning: ZyBooks provides instant feedback on MSPs; you see failing tests and can resubmit; there is a full history of submissions; no copy-paste from external sources; all work should be done within ZyBooks editor.
  • MSPs emphasize small, iterative coding tasks to improve practice and reduce risk of failing a single large project.
  • Progress tracking: the textbook shows per-section progress; assignment due dates are shown in the LMS; sections tie to specific MSPs and weekly due dates.
  • Additional notes: you write code in ZyBooks for MSPs, not in a separate IDE in the textbook; some MSPs include live code execution within ZyBooks; instructors/grading staff review code history and ensure integrity.

Recitations and Labs (90-minute sessions)

  • Purpose: provide guided practice and help with questions; reinforce material from lectures and textbook; not a substitute for homework.
  • Scheduling and access:
    • 16 sections available this semester; online students have preference for Zoom-based sections.
    • Recitation scheduling uses a Qualtrics survey to group students by schedule and experience level.
    • Recitations run Wednesday, Thursday, and Friday; times span from morning to evening (9:00–19:00) to accommodate different student needs.
  • Structure:
    • Recitations start in Week 2; include a brief material highlight, time for questions, and a problem to work on (group or solo).
    • Activities emphasize practical programming tasks similar to MSPs but with more guided instruction.
  • Demographic-based grouping: more information in the Qualtrics survey helps tailor sections to experience level (e.g., fewer beginners in some groups, more experienced in others).
  • Important caveat: recitation is not for doing textbook work; it focuses on quick questions and a guided programming task.

Weekly Schedule and Cadence

  • Typical week structure (Sunday–Saturday):
    • Sundays: at least one lecture’s worth of textbook work due (participation and challenge activities).
    • Tuesdays/Thursdays: recitations (scheduled times) and related coursework.
    • Thursdays: MSPs due as part of the weekly rhythm.
    • Saturdays: progress checks due (open all week; due on Saturdays; 5-point MC questions; two attempts allowed; best score counts).
  • Three-week build model for topics:
    • Week A: topics 1 and 2—textbook participation/challenge; recitation; progress checks.
    • Week B: topics 1 and 2—recitation and continued progress checks; Week C: MSP work for topics 1 and 2.
    • Week D: topics 3 and 4—textbook participation/challenge; recitation; progress checks; MSPs in subsequent weeks.
  • Estimated weekly time commitment: about 4!−5 hours4!-5\text{ hours} per week, with some weeks shorter or longer depending on MSP load.
  • Rhythms are designed to foster continuous engagement, with multiple due dates each week to improve accountability and time management.

Quizzes

  • Quizzes account for 30% of the grade; there are five quizzes (each ~6%).
  • Characteristics:
    • Open for three days each window; open-note/open-resource; not open AI.
    • Each quiz covers roughly four to six lectures worth of material.
    • No comprehensive final; the quizzes collectively cover the semester material.
  • Due dates and access: due dates and details are posted in the Canvas syllabus page; example: Quiz 2 due Saturday, March 8 (Spring 2025).

Academic Integrity and AI Policy

  • All MSPs and text-work assessed via ZiBooks integrity checks to compare student submissions for potential violations.
  • AI usage policy:
    • AI (e.g., ChatGPT, Copilot) may be used for clarification of material, but not for generating solutions to homework or quiz problems.
    • Writing solutions with AI is prohibited in CS1 assignments; there is an official policy in Week 1 detailing allowed practices.
  • Penalties for academic dishonesty (tiered):
    1) First offense: meeting with instructor; up to 50% maximum grade on the assignment or quiz.
    2) Second offense: meeting with instructor; a penalty of -25% to the overall grade.
    3) Third offense: meeting with instructor plus course failure.
  • Rationale: human review and domain expertise are essential for safety and integrity of code, especially for critical systems (medical devices, self-driving cars, banking software).
  • The instructor emphasizes that integrity is foundational to learning; AI can be a learning aid for clarification, but not a substitute for understanding and original work.

Class Demographics and Encouragement

  • The class comprises a mix of majors and non-majors across disciplines (CS, MIS, Cyber, Studio Art, Library Science, Environmental Science, Economics, Biology, Math, Physics, Chemistry, Bioinformatics, Engineering, etc.).
  • Emphasis on cross-disciplinary applications of computing and the value of programming in various fields.
  • The instructor expresses enthusiasm for student projects and real-world applications, inviting students to share their own programming projects and experiences.

Tools, Access, and Setup

  • Platforms:
    • Canvas: course management and grading portal.
    • ZyBooks: online textbook with integrated activities and MSP editor.
    • PhonE: IDE to be installed for lab work and recitations.
  • First Day Access: automatic textbook access billed to student account; sign-up is required; no paper textbook.
  • Videos and supplementary materials: section-specific videos accompany textbook sections; videos are optional but provide alternative learning modes.

Next Steps for Students (What to Do Next)

  • Sign up for the ZyBooks textbook via Week 0/Week 1 module in Canvas.
  • Complete the Recitation Planning Survey (Qualtrics) to schedule a recitation section.
  • Begin working through the early lectures in the ZyBooks (Lectures 2–4 in the initial weeks).
  • Review the syllabus page in Canvas for due dates, grading details, accommodations, academic integrity, late-work policies, and other key policies.
  • Reach out to the instructor with questions via Canvas inbox, email, or office hours (office hours are by appointment using TidyCal).

Final Encouragement and Contact

  • The instructor emphasizes that success comes from engagement, timely submissions, and active participation in recitations and MSPs.
  • If you have questions, you’re encouraged to ask via Canvas, email, or during scheduled office hours.
  • Welcome to the course; the instructor looks forward to helping you learn and apply CS concepts across disciplines.