Grading policy and submissions

Back to the Methods of Teaching Data Science course

Back to Technion Data Science Education website

To earn the course credits and a grade, students had to meet the following requirements:

  • Participate in all synchronous lessons.
  • Submit all assignments on time (see Table 1). Late submissions are not accepted.
    • All assignments are accessible from the onset of the semester and students were invited to start working on them from the beginning of the course (and even before).
  • Facilitate an asynchronous lesson (see Table 2). Each week, one student is responsible to teach an asynchronous lesson and to report on it at the beginning of the following lesson (for up to 15 minutes).
    • In the asynchronous lessons, all students worked on an activity that either the student or (in most cases) we chose. In addition to leading the task, the students were also requested to reflect on their experience teaching an asynchronous lesson.
  • Complete three questionnaires during the semester. Preliminary questionnaire, mid-semester questionnaire, end-of-the-semester questionnaire.

Table ‎1 . The course assignments

Assignment # Description Percent of grade
(out of 100)
Submission due
1 Watch pre-recorded lectures on data analysis with Python, on the following topics (available on the course website – see the Technion Data Science Website):

•       Introduction to data science

•       The data science workflow

•       Colab environment

•       Pandas

•       Exploratory data analysis (EDA)

•       Introduction to machine learning

A. Write a personal reflection on the lectures / your learning process / learning and teaching in the flipped classroom format / challenges of teaching and learning data science.

B. Suggest five topics that you would like to learn in the course and explain your suggestions.

The submitted assignment is limited to two pages.

15 2nd week of the course
2 Watch the following lectures on KNN and machine learning (see the Technion Data Science Website):

•       The KNN algorithm

•       Performance indicators

•       Basic data science concepts: Confusion matrix, overfit and underfit, model complexity vs. generalization

•       Descriptive statistics

•       Python libraries: Seaborn

Read the following paper that demonstrates how to teach the basic concepts of data science with one simple algorithm – KNN:

Hazzan, O., & Mike, K. (2022). Teaching core principles of machine learning with a simple machine learning algorithm: The case of the KNN algorithm in a high school introduction to data science course. ACM Inroads 13 (1), 18-25.

The article is available on the course website.

For each level of Bloom’s cognitive taxonomy, develop two questions on the KNN algorithm (12 questions in total). The paper the students were asked to read (Hazzan & Mike, 2022) presents examples of questions on each level of Bloom’s taxonomy.

15 3rd week of the course
3 Choose another machine learning algorithm and learn about it using available online resources.

(a) Why did you choose this algorithm? Which additional algorithms did you consider?

(b) Describe the resource(s) you used to learn the algorithm you chose, and reflect on your learning process of the algorithm.

(c) For each level of Bloom’s cognitive taxonomy, develop two questions on the algorithm you chose (12 questions in total).

15 4th week of the course
4 A research in data science education to be submitted in four stages:
(a) Carry out a mini data science research project on any educational data you choose. Submit the research notebook containing the research description and the data analysis. Stage (a) – 15 7th week of the semester
(b) Record a video clip that describes your research and upload a link to the video clip to the designated forum in the course website. The video clip should not exceed 10 minutes. Stage (b) – 15 9th week of the semester
(c) Watch at least three of your classmates’ video clips and give each of them feedback as a response to the forum message that presents the link to the video clip. Stages (c) & (d) – 5 10th week of the semester
(d) Write 10 insights you had while watching the video clips. 10th week of the semester
An example of a data resource:

Choose a paper / report / curriculum about data science education.

Prepare a presentation on the paper / report /
curriculum you chose.

Present the paper / report / curriculum in the lesson – up to 15 minutes per presentation.

Following the presentation, reflect on the learning process in the course, in general, and on the work process of this task, in particular.

Submit the presentation including a reflection slide at the end.

20 11th and 12th weeks of the course


Table 2. List of asynchronous tasks

# Asynchronous tasks

Each week one student leads the execution of an asynchronous task and presents it in the following lesson.

Task given Presentation date in class


Attend the Faculty Seminar on Sunday, March 20, 2022 at 2:30 pm in Room 307 or by Zoom. In the lecture, Koby Mike (the course TA) will present his doctoral thesis on data science education (a link to a recorded lecture will be published later on the website).

The assignment:

a) From the topics presented in the lecture, choose three topics that you would like to focus on in the course and explain your choice.

b) Reflect on the process of leading an asynchronous lesson.

1st week 2st week
2 a) Is computer science an interdisciplinary field? Is the interdisciplinarity of computer science similar to the interdisciplinarity of data science? Explain your claims.

b) Propose ten exercises / assignments that emphasize the interdisciplinarity of computer science. The exercises / assignments should include at least four types and should be suitable for high school pupils.

c) Reflection on the process of leading an asynchronous lesson.

2nd week 3rd week
3 a) Use the platform to discuss the following topics:

  1. The “Questions Workshop” held during Lesson #4;
  2. The use of the platform for class discussions;
  3. Data science ideas and concepts applied in

b) Reflect on the process of leading an asynchronous lesson.

4th week 5th week
4 a) Prepare a presentation of up to 20 minutes on computational thinking and statistical thinking. The presentation must include various multimedia means, such as video clips, games, texts, examples of questions, a list of references, etc.

b) Reflect on the process of leading an asynchronous lesson.

5th week 6th week
5 a) Prepare a presentation according to Exercise ‎2.15 “Overview of the history of data science”.

b) Reflect on the process of leading an asynchronous lesson.

6th week 7th week
6 a) Prepare a presentation according to Exercise ‎2.14 “Comparisons of data science codes of ethics”.

b) Reflect on the process of leading an asynchronous lesson.

7th week 8th week
7 a) In Assignment 3 (see Table ‎18.1), four different machine learning algorithms were selected. Prepare one presentation of 40 minutes that includes:

  1. Presentation of the four algorithms.
  2. The process of constructing the presentation, the pedagogical considerations that guided the organization of the presentation, etc.

b) Reflect on the process of leading an asynchronous lesson.

8th week 9th week
8 a) Challenges and teaching methods involved in mentoring a machine learning / data analysis project in high school.

b) Reflect on the process of leading an asynchronous lesson.

11th week 12th week