Mastering Blackboard: A Comprehensive Guide to CUHK's Learning Management System
Blackboard is an integral part of the e-learning experience at The Chinese University of Hong Kong (CUHK). It enhances traditional learning by providing a virtual space where students can engage with course content, collaborate with peers, and access a variety of resources anytime and anywhere. This article provides a comprehensive tutorial on using Blackboard at CUHK, covering various aspects from accessing course materials and grades to utilizing discussion forums and other e-learning tools.
Introduction to Blackboard at CUHK
Blackboard serves as the central online learning management system at CUHK. Faculty can easily upload materials, create interactive assessments, and facilitate discussions, making the learning process more dynamic and accessible. Additionally, Blackboard integrates various tools that support online testing, grading, and communication, enriching the overall educational experience.
Accessing and Navigating Blackboard
CUHK's Blackboard system is designed to not only support students' academic needs but also to foster an engaging community of learning. The CUHK e-learning system prides itself on being user-friendly, catering specifically to the needs of new users. Its interface is intuitive, allowing learners and instructors to find what they need without extensive training. Upon first login, users are greeted with a welcome guide that highlights key features and functionalities. Additional support is available through tutorials, frequently asked questions, and direct technical assistance. Users can easily access course materials, participate in discussions, and monitor their progress through a centralized dashboard. By prioritizing user experience, the CUHK e-learning system ensures that individuals can quickly become comfortable and productive within the learning environment, making it an effective choice for both academic and professional training purposes.
Key Features and Functionalities
Course Content and Materials
Blackboard allows instructors to upload a variety of course materials, including:
- Slides: Slides will be available the day before the lecture day.
- Micro-modules: Micro-modules coupled with Blackboard online discussion forums as a way to extend the tutorial discussions.
- Assignments: It's great for submitting assignments and getting feedback from lecturers easily.
- Supplementary materials: Students can access all course-related materials in one place, which makes studying much more convenient.
Communication and Collaboration
- Discussion Forums: Online discussion forums as a way to extend the tutorial discussions. Blackboard is an online learning management system that helps both faculty and students. We will use Piazza (AIST4010) for discussion. You can ask questions through Piazza, even anonymously. For a personal matter, please use the private post to the instructor and the TA.
- Announcements: Instructors can post announcements to keep students informed about important updates and deadlines.
Assessments and Grading
Blackboard provides tools for creating and managing various types of assessments:
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- Assignments: Students can submit assignments electronically through Blackboard.
- Quizzes: In-class quiz (5%): One in-class quizzes. The questions will be simple. Mainly for checking the participation. The quiz is open-booked.
- Exams: A grading midterm exam. All exams and quiz are open-book. You are allowed to take any paper-based materials. However, no phone or computer is allowed. Other communication tools are also not allowed. Half of them will be fixed-answer questions while half of them will be Kaggle competition. The last Kaggle competition is optional.
- Grades: Your grades can appear in multiple places. Wherever you find your grade, you'll find the information you need. Your grades can be accessed:
- On your course's Grades page inside the relevant course
- In your activity stream
- On your global Grades page, accessed from the list where your name appears
Inside the course, you can access your course grades on the course's navigation bar. Select the Grades icon to access all the coursework that's specific to the course you're in. Then, on the Course Grades page, select a title to see the grade and feedback from your instructor.
As your instructors post grades, you'll find them in your activity stream. Select View your grade to display your grade. If your instructor added feedback, you'll see it after the item's title. If your instructor updates a grade, the grade is also updated in the stream.
To see your grades for all your courses in one place, select Grades from the list on the left where your name appears. All of your grades are organized by course. You can see what's due and set priorities across all of your courses. No need to navigate to each course individually.
Grade Pills
Your instructor determines how to display your grade for each graded item: Letter grade, Points, or Percentage. The grade pill for each assessment question and graded item may appear in colors or with dark backgrounds. For the colored grade pills, the highest score range is green and the lowest is red. The colors map to these percentages:
- > 90% = green
- 89-80% = yellow/green
- 79-70% = yellow
- 69-60% = orange
- 59-50% = red
Your institution can disable the color scheme for all courses. The grade pills appear with dark backgrounds and white grades. Colors won't be used to convey performance.
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Course-Specific Information and Policies
Deep Learning Course Example
This course covers how to use deep learning techniques to resolve real-life computational problems, handling different kinds of data. We start the course by introducing the problem-solving paradigm with deep learning: data preparation, building the model, training the model, model evaluation, and hyper-parameter searching. Then, we fill in the details in the paradigm. Regarding the deep learning models, we will go from the simplest linear regression model, towards the relatively complicated models. To handle various data types, that is, the structured data, images, text, sequences, signals, and graphs, in our daily life, we would cover CNN/ResNet, RNN/LSTM, Attention, and GNN models. In addition to the above paradigm, we will also cover the commonly used techniques to handle overfitting.
Instructors and Teaching Assistants:
- Yu LI (liyuATcse.cuhk.edu.hk), SHB-106
- TA: Licheng ZONG (lczongATlink.cuhk.edu.hk), SHB-1026
Lecture and Tutorial Schedule:
- Monday: 2:30pm-4pm, ERB-712
- Thursday: 2:30pm-3:15pm, YIA-503
- Thursday: 3:30pm-4:15pm, YIA-503
Grading Breakdown:
- Scribing (6%): Grading scribing. Summarize one of the lectures. Submit it within one week after the course. Each student should do at least one lecture.
- In-class quiz (5%): One in-class quizzes. The questions will be simple. Mainly for checking the participation.
- Midterm (15%, Cancelled): A grading midterm exam. One bonus question (1%).
- Project (47%): A grading project. You should submit a proposal (6%), a mid-term report (7%), a final report (17%) and give a presentation (17%).
- Bonus (up to 2.5%): One bonus question in Midterm (1%). One additional scribing: 1%. Pre-course survey + Post-lecture survey: 0.3% for each, and the maximum is 1.5%. I do encourage you to complete all of them so that to let me know your feedback and adjust the course accordingly.
Assessment Policies:
All exams and quizzes are open-book. You are allowed to take any paper-based materials. However, no phone or computer is allowed. Other communication tools are also not allowed. Half of them will be fixed-answer questions while half of them will be Kaggle competition. The last Kaggle competition is optional. The TA will set up a baseline using linear regression and simple deep learning. If you are better than the baseline, you can be above 60%. The final score of each student will be based on the ranking. The first one gets 100%, and the last one above the deep learning baseline gets 80%.
Programming and Tools:
Python or any other you are familiar with. For python, we suggest you to use Colab.
Scribing:
Please sign Scribing preference. We should have at least one student for each lecture. We may adjust the assignment if necessary. Notice that your note and scribing will be posted online, for others reference. You can choose to remove your name or not. Deadline for signing the scribing: 11:59 pm on 17th Jan.
Project Details:
You can choose to do the project individually or team-up. However, for the team-up project, we will have higher requirement and the project should target at publication. Moreover, the contribution of each student and the workload split should be defined clearly at the beginning of the project. You should submit a proposal (5%), a mid-term report (6%), a final report (16%) and give a presentation (16%).
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Late Submission Policy:
Each student will have 6 late days to turn in assignments, which can be used on A1, A2, A3, project proposal, and project M-report. They cannot be used on the project final report and the scribing note. A maximum of 2 late days can be used for each assignment.
Surveys:
Deadline for each survey: 11:59pm on the day before the next lecture. We do this because I could have time to answer the questions you mentioned in the survey. Please fill 1 in the Google sheet: Survey results, once you have finished one survey. Usually, we will trust the 1s you fill in the Google sheet.
Another Deep Learning Course Example
Lecture and Tutorial Schedule:
- Tuesday: 4:30pm - 6:15pm, ERB 804
- Thursday: 4:30pm - 5:15pm, MMW 705
- Thursday: 5:30pm - 6:15pm, MMW 705
- Thursday: 3:30pm - 4:15pm, MMW 705
Grading Breakdown:
- Scribing (5%): Grading scribing. Summarize one of the lectures. Submit it within one week after the course. Each student should do at least one lecture.
- In-class quiz (5%): One in-class quiz. The questions will be simple. Mainly for checking the participation.
- Project (48%): A grading project. You should submit a proposal (6%), a mid-term report (8%), a final report (17%) and give a presentation (17%).
- Bonus (up to 2%): One additional scribing: 1%. Pre-course survey + Post-lecture survey: 0.2% for each, and the maximum is 1%. I do encourage you to complete all of them so that to let me know your feedback and adjust the course accordingly.
Assessment Policies:
The quiz is open-booked. Half of them will be fixed-answer questions while half of them will be Kaggle competition. The last Kaggle competition (A3-Kaggle) is optional. The TA will set up a baseline using linear regression and simple deep learning. If you are better than the baseline, you can be above 60%. The final score of each student will be based on the ranking. The first one gets 100%, and the last one above the deep learning baseline gets 80%.
Programming and Tools:
All the programming assignments should be done by Python, and we suggest you to use Colab.
Scribing:
Please register for your Scribing preference. We should have at least one student for each lecture. We may adjust the assignment if necessary. Notice that your scribing note will be posted online, for others’ reference. You can choose to hide your name or not. Deadline for registration: 11:59 pm on Sept. 18 (Thu) . After that, the Google sheet will be closed.
Project Details:
You can choose to do the project individually or team-up. However, for the team-up project, we will have higher requirement and the project should target at publication. Moreover, the contribution of each student and the workload split should be defined clearly at the beginning of the project. Please discuss with Prof. You should submit a proposal (6%), a mid-term report (7%), a final report (17%) and give a presentation (17%).
Late Submission Policy:
Each student will have 6 late days to turn in assignments, which can be used on written assignments including A1-written, A2-written, A3-written, project proposal, and project M-report. They cannot be used on Kaggle assignments, the project final report and the scribing note. A maximum of 2 late days can be used for each assignment. Grades will be deducted by 25% for each additional late day.
Surveys:
Deadline for each survey: 11:59pm on the day before the next lecture. We do this because I could have time to answer the questions you mentioned in the survey. Please fill 1 in the Google sheet: Survey results, once you have finished one survey. Usually, we will trust the 1s you fill in the Google sheet.
Additional E-Learning Tools
Beside Zoom and Panopto, what else? A sharing on using online discussion forum and game-based learning. For this semester, I have also used Zoom and YouTube to live broadcast myself playing the game and at the same time explain the meaning behind it.
Tips for Effective Use of Blackboard
- Regularly check for announcements: Stay updated on course-related news and deadlines.
- Participate in discussion forums: Engage with your peers and instructors to enhance your understanding of the material.
- Submit assignments on time: Adhere to deadlines to avoid penalties.
- Review feedback: Use instructor feedback to improve your performance on future assignments.
- Utilize available resources: Take advantage of tutorials and support materials to maximize your use of Blackboard.
Blackboard Beyond the Classroom: Employee Training
Companies can use the CUHK e-learning system to conduct remote training sessions, ensuring that all employees have access to necessary resources no matter their location. The system also allows for tailored training programs, accommodating various learning speeds and styles. Furthermore, tracking tools help HR and trainers monitor employee progress and engagement, enabling data-driven adjustments to training regimens.
E-Learning vs. Blackboard
They are related but not the same. E-learning is a broader concept that encompasses all forms of electronic learning, while Blackboard is a specific learning management system (LMS) used to deliver e-learning content.
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