0.1.1. Read the Course Syllabus¶

To get started, let’s go through the course policies described in the course syllabus on Canvas to make sure you understand how the course will run, what your responsibilities are, how you will be graded, and how you should conduct yourself in class.

Before continuing, please read the course syllabus posted on Canvas. This chapter highlights the core workflows, grading mechanisms, and expectations you need to succeed from day one.

0.1.2. Who Is This Class For?¶

This course teaches introductory programming in Java, with a significant emphasis on object-oriented software design and on software testing. In many ways, it is comparable to the AP Computer Science A course offered at some high schools. It is taken by students majoring in Computer Science, minoring in Computer Science, and also by students in other majors across the university.

It can be very helpful if you have already had some exposure to programming. Basic familiarity with variables and sequential logic will give you a leg up. However, the course is designed to be approachable by beginners who do not have any prior programming background.

0.1.3. Students of Many Experience Levels¶

While this course is designed and intended for beginners, please be aware that students from a very wide range of backgrounds take this course, including some students who may have significant amounts of prior programming experience. If you are a student with significant prior programming experience who has taken the AP Computer Science A course and received a score of 4 or 5, we strongly recommend you use your AP credit to enroll in CS 2114 instead.

For other students, just remember that while there are many reasons why students who already know how to program might take this course, the course isn’t intended for that audience–they are overqualified. You may see such students in lab or on Piazza, asking and answering questions that appear to require advanced programming knowledge. The course staff are happy to answer such questions, but please remember that we do not expect beginners in this course to possess that level of knowledge or to perform as if they have already learned how to program prior to taking this course.

Students with extra experience should not set your expectations about what is normal or what course staff expect of the bulk of students in this course who are learning to program for the first time.

0.1.4. Weekly Schedule¶

This course runs on a weekly rhythm, and learning the schedule will help you develop a routine, anticipate deadlines, and stay on track. It is extremely important to stay on schedule and turn your assignments in on time every week. The single biggest reason for falling behind in this course is not starting to work on assignments in advance, missing deadlines, and letting work accumulate.

In particular, while the semester starts out with introductory topics, please do not allow yourself to be lulled into believing you can cut corners on readings or lab preparation. Regular, weekly engagement with the course content and active practice on your programming skills are necessary to develop true mastery of the concepts.

Throughout the week, pay attention to the following recurring activities:

• Sunday (11:59 PM): Weekly Reading Activity due on Canvas (complete before your lab).

• Before your Lab: Complete the required online Pre-Lab Assessment on Canvas.

• Monday / Wednesday (or Tuesday / Thursday): Lecture meetings with active in-class problem solving and iClicker activities.

• During your Scheduled Lab Period (Thu/Fri/Mon/Tue/Wed): Attend your enrolled lab section, collaborate with your assigned partner, and submit your completed lab before the period ends.

• Wednesday (11:59 PM): Program Assignment due (every other week).

• Every Other Week: Complete your 20-minute proctored Quiz in the Secure Computer-based Testing Facility during your scheduled testing window.

• Within 2 Weeks of Program Deadlines: Complete your required 5-minute in-person Grading Interview with a TA.

0.1.5. Attendance and In-Class Participation¶

You are expected to attend all lecture and lab meetings, and strive to arrive promptly on time.

• Lecture Meetings: While traditional attendance is not taken at the door, lectures incorporate frequent interactive problem solving and peer discussions using the iClicker Student Mobile or Web App. Graded in-class participation accounts for 90 points of your final course grade and cannot be made up remotely. You must bring your laptop and iClicker-enabled mobile device to every lecture. A small cushion for unexpected absences (such as minor illness) is already factored into the participation grading model.

• Lab Meetings: Attendance in your officially enrolled lab section is mandatory. You must be physically present in the lab room to submit lab work. If an unavoidable illness or emergency prevents attendance, you must use a Lab Grace Pass to complete and submit the lab on your own time.

• Final Examination: The final exam date and time are set by the university. Only the Dean’s office can grant permission to reschedule a final exam (e.g., in cases of direct exam conflicts or having 3+ exams in a 24-hour window).

Note: If you have an evening lab session that goes past 5pm and are also taking a math or physical science course, you should immediately double-check with your instructor in your math or science course to find out if they schedule required, proctored exams or other obligations during your scheduled lab period later in the semester. Out of class exams from other courses are not an excuse to miss your mandatory lab meeting time. To avoid this situation, you can switch to a different section of this course during the first week of classes to change your lab meeting time. Alternatively, if you discover such a conflict later in the semester, you must work with your math or science instructor to arrange an alternate time to take their exam.

0.1.6. Reading Activities¶

There is no physical paper textbook to purchase for this course. Instead, all reading materials and interactive modules are presented through weekly online Reading Activities on Canvas, which are due on Sunday nights at 11:59 PM.

• Preparation for Labs and Quizzes: In addition to Sunday deadlines, you should always complete the week’s reading before your scheduled lab meeting to be prepared for the required pre-lab and to be prepared for the lab work itself. The concepts introduced in reading activities are also directly assessed on upcoming quizzes and programming assignments.

• Unlimited Practice for Full Credit: Reading activities contain interactive coding questions to help you practice. They also include supplementary syntax practice resources for those new to programming, as well as addiitonal exercises for self-study and exam preparation. You can repeat these exercises as many times as you like before the deadline until you earn full credit.

• Strict Due Dates: Reading activities must be completed by the posted deadline. No late submissions are accepted on Reading Activities. You can continue to review or complete all exercises (both graded and supplemental) for self-study purposes after the deadline (not for grade credit).

0.1.7. Lab Assignments¶

Each week includes a hands-on laboratory assignment designed to reinforce the concepts you are learning. Labs are posted on Canvas at the start of the week, and your work is submitted online and evaluated by the automated grader.

Key lab policies and workflows include:

• Mandatory Pre-Lab Assessment: Every lab includes a pre-lab assessment on Canvas covering the required background and instructions. You must complete the pre-lab with 100% accuracy (no errors) before your lab period starts. You may repeat the pre-lab as many times as needed, but you will not be allowed to begin the lab assignment until you have achieved a perfect score.

• In-Person Attendance and Pair Collaboration: You must attend your enrolled lab section in person. Each week, you will be paired with a lab partner to collaborate, discuss approaches, and troubleshoot code.

• Punctuality: You must arrive on time so that you and your partner can begin immediately. Students who arrive more than 15 minutes late will have their partner reassigned, will not be permitted to submit work during the lab period, and will need to complete the lab independently using a Lab Grace Pass.

• Immediate Feedback & Resubmission: You may submit your lab code to the autograder as many times as you like during your lab period to receive feedback, fix errors, and improve your score. Only your most recent submission is evaluated.

• TAs as Coaches, Not Hand-Holders: Lab TAs are available to answer conceptual questions and steer you past roadblocks. However, they expect you to have read the assignment and completed the pre-lab beforehand. If you arrive unprepared, you will struggle to complete the work in time.

• Specifications Grading (EMRN): Labs are evaluated on the EMRN scale. Achieving a rating of Meets Expectations (M) earns full credit (9 points per lab, 126 points total for 14 labs), while Excellent (E) earns 110% extra credit.

• Check-Out Requirement: You and your partner are working as a team. You may not leave the lab early until both you and your partner have completed all programming tasks, submitted your work, and officially checked out with a lab TA.

• Individual Accountability & Lab Oral Interviews: While you work in pairs during lab meetings, all lab submissions are strictly individual work and must be submitted under your own account. Every student is individually accountable for understanding every line of code they submit. Course staff may randomly call in any student for a brief oral grading interview on any lab assignment to assess their code comprehension, ability to trace execution, and design justification. Demonstrating satisfactory understanding of your submitted solution is required to confirm your mastery credit.

• Lab Late Policy & Grace Passes: Lab submissions close at the end of your scheduled lab period. Late work is not accepted without using a Lab Grace Pass. You begin the semester with 3 Lab Grace Passes, each of which grants a 48-hour deadline extension past the end of your scheduled lab meeting. Each lab assignment may only be extended once.

0.1.8. Program Assignments¶

There are 6 major programming assignments given throughout the semester, posted every other week on Canvas and due on Wednesday nights at 11:59 PM. Programming assignments require you to synthesize course concepts, design object-oriented software solutions, and write comprehensive automated test suites.

• Independent Work: Program assignments are strictly individual work done outside of class. You must design, write, test, and debug your own code. You may not collaborate with other students or copy code from any source.

• Submissions & Automated Feedback: You submit your code electronically to Web-CAT, where it is evaluated against automated test suites for correctness, code style, and test coverage. You can submit as many times as you like prior to the deadline to review feedback and refine your code. Only your last submission is graded.

• Specifications Grading (40 Points Each): Each programming assignment is worth 40 points (total of 240 points across 6 projects) and is evaluated on the EMRN scale. Earning an M (Meets Expectations) awards 100% (40 points), while an E (Excellent) awards 110% (44 points).

0.1.9. Quizzes¶

Rather than traditional high-stakes midterm exams, this course uses 7 shorter, proctored quizzes given approximately every other week. Each quiz is worth 35 points (for a total of 245 points toward your course grade).

• Secure Computer-Based Testing Facility: To ensure testing integrity and flexibility, quizzes are administered out of class in a dedicated, secure computer-based testing facility on university-provided desktop computers.

• Scheduling Your Quiz: You will schedule your own testing appointment at the facility during the designated multi-day window for each quiz.

• Testing Environment: Quizzes are strictly closed-book and closed-notes. No personal laptops, phones, smartwatches, or outside materials are permitted in the testing facility. (If the testing facility is unavailable for a specific quiz, it will be administered in class using LockDown Browser with advance notice.)

• Quiz Mastery Passes (Retakes): - Students begin the semester with 3 Quiz Mastery Passes. - If you miss a quiz or score lower than you hoped, you can use a Quiz Mastery Pass to schedule one retake of that quiz in the testing facility during the week after quiz results are released. - Each individual quiz can be retaken at most once.

0.1.10. Programming Language and Environment¶

This course teaches object-oriented programming using Java.

• BlueJ Development Environment: We will use the BlueJ IDE for code development, interactive object visualization, and lecture examples.

• Download Directly from Canvas: Please install BlueJ using the installer link provided on the course Canvas homepage rather than downloading from the generic BlueJ website. The Canvas installer includes specialized support libraries and testing tools pre-configured for CS 1114.

• Web-CAT & CodeWorkout: Automated grading and code feedback are provided through Web-CAT (for lab and program assignments) and CodeWorkout (for interactive practice exercises).

0.1.11. Cheating and The Virginia Tech Honor Code¶

Academic integrity is taken extremely seriously at Virginia Tech. All submitted coursework in CS 1114 is considered graded work covered by the Honor Code. All program assignments, reading exercises, pre-labs, and quizzes must be completed individually.

0.1.12. AI and Long-Term Learning¶

Generative AI tools like ChatGPT, Microsoft Copilot, Gemini, Claude, Github Copilot, and any many others, are extremely common and their use is becoming a regular occurrence for many. In this class, we want to make sure you understand when and how we expect you to use generative AI.

Your goal in CS 1114 is not simply to produce working code, but to develop the ability to analyze problems, design solutions, and debug software independently. Generative AI tools (such as ChatGPT, Claude, Gemini, Copilot, and Hokie AI) make it easy to generate correct-looking code without engaging in the underlying reasoning. This is known as cognitive offloading–handing off the mental work that builds your own problem-solving capacity, intuition, and technical skill.

If you cannot independently explain, reproduce, and defend your code during your in-person TA grading interviews, you have not done the learning. Future computer science courses, technical interviews, and your professional career depend entirely on your foundational ability to reason through code independently.

At the same time, there are two very powerful things you can use generative AI for that you should really consider: studying and becoming a better student. On the studying side, there is a lot of benefit to studying in a group with other students, asking each other questions by referring to your notes or the reading assignments, and helping each other understand the material. Turning studying, particularly if you are reviewing before a quiz or a test, into a social activity is a powerful technique. But there is also a lot of potential to use generative AI prompts to create self-study quizzes for you (and your study group) to check your own knowledge on concepts we are teaching. Occasionally during this course, we will suggest possible prompts you can use in this way, whether it is about creating self-test questions you can use to study, or creating flash cards, or trying to identify possible misconceptions you might have, or whatever.

Second, generative AI tools can big help in learning how to be a better student. Whether you want an AI-generated work schedule for how to regularly spend time on all your assignments, or AI coaching on better note taking skills, or AI advice on how to prioritize work across different classes so you know what to focus on while also knowing how you’ll complete many assignments all by their deadlines without crushing yourself, there are many practical ways you can use generative AI. Again, we will occasionally look at some of these ways throughout the course.

0.1.13. AI Use Policy: Learning Support Only¶

Students may use university-approved generative AI tools solely as a conceptual learning aid. For the purposes of this course, you should consider generative AI tools just as if they were “other people” outside the classroom, or even “fellow students”. As stated in the syllabus, you may freely offer and receive verbal assistance with your classmates on many topics, including: how to use the programming language, what library classes or methods do, what errors mean, or how to interpret assignment instructions. These same rules apply to generative AI tools–you can ask questions or consult generative AI resources for any of these purposes, just like you can your peers.

However, treating generative AI tools is the same way as other people with respect to the Honor Code means that, as with fellow students, you may not use them to receive help while working on your program code or any graded assignments. You may not show your code to others or view code written by others, including generative AI tools, when working on assignments to turn in for this course. In the same way, you cannot copy code from them (or another person) for any graded work.

Student Responsibility: You are responsible for the factual and logical accuracy of any AI-assisted explanation you consult. If you are ever unsure whether a particular use of AI complies with course policy, consult your instructor before proceeding.