Teaching Dossier

Golam Rabbany

Teaching Dossier, 2022

GR-Dossier.pdf

1. Teaching Philosophy

"Teaching is not a profession but a responsibility to build a life and awaken joy in creative idea with knowledge."

-Golam Rabbany

I believe that all student is unique and have something special that they can bring to their own education. I will assist my students to express themselves and accept themselves for who they are, as well embrace the differences of others.

"Every classroom has its own unique community; my role as the teacher will be to assist each child in developing their own potential and learning styles. I will present a curriculum that will incorporate each different learning style, as well as make the content relevant to the students' lives. I will incorporate hands-on learning, cooperative learning, projects, themes, and individual work that engage and activate students learning."

2. Student Engagement Strategies

Teaching Approach

Teachers have an enormous responsibility in forming the values of student. Teaching approach is a set of principles, beliefs, or ideas about the nature of learning which is translated into the classroom. My approach is different from others. It must be following aspects –

• Contextual Learning: It engages student in rethinking what it means to be human in relation to God, other people and cultures, and the whole of creation.

• Process Oriented Guided Inquiry learning (POGIL): Which is support me with the implementation of student-centered learning environments like “Active group discussion, discover concepts during class, reinforce after class”.

• Project Based Learning (PBL): An approach to teaching that focuses primarily on having students engage in explorations of real-world problems and challenges.

• Reality Pedagogy Learning: Reality pedagogues/teachers believe that, for teaching and learning to happen, there has to be an exchange of expertise between students and teacher.

3. Standard lesson plan

3. A Sample Course Description:

3.1 Course Description

An introduction to data mining and machine learning; Data preparation, model building, and data mining and machine learning techniques such as clustering, decisions trees and neural networks; Induction of predictive models from data: classification, regression, and probability estimation using modern tools like Sklearn, Jupyter, CoLab, etc.

3.2 Course Syllabus

Course Code : CSE 321

Course Title : Data Mining and Machine Learning and Lab

Program : B.Sc. in CSE

Faculty : Faculty of Science and Information Technology(FSIT)

Semester : Summer 2022 Year: 2022

Credit : 4.0 Contact Hour: 4.5

Course Level : Level 3 Term 2

Course Category : Core Engineering

Instructor Name : Eng. Golam Rabbany,

Designation : Lecturer

Email : rabbany.cse@diu.edu.bd

Office Time : Sunday to Thersday (Time: 09:00 am to 05:00 pm)

Office Address : Room 505, Academic Building-04, Department of CSE, Ashulia Campus.

3.3 Class Schedule

Day & Time : Saturday and Monday( 8:30 am to 11:30 pm ).

Tuesday and Thursday (10:00 am to 01:00 pm)

Room : 505, AB 04.

3.4 Course Rationale

An introduction to data mining; Data preparation, model building, and data mining techniques such as

clustering, decisions trees and neural networks; Induction of predictive models from data: classification,

regression, and probability estimation; Application case studies; Data-mining software tools review and

comparison.

3.5 Pre-requisite Topics:

Programming : C, python.

Mathematics : High school algebra

Programming environment : CodeBlocks / Eclipse/ VPL /any compiler / Any interpreter

3.7 Expected Learning/ Course Outcomes (CO):

at the end of the course, student will be able to do:

CLO1 : Able to conceptualize basic applications, concepts, and techniques of data mining and machine learning.

CLO2 : Able to identify appropriate data mining and machine learning algorithms to solve real world problems.

CLO3 : Able to compare and evaluate different data mining and/or machine learning techniques like classification, prediction, clustering and association rule mining.

CLO4 : Able to apply knowledge of data mining and machine learning in developing research ideas.

CSE 231: Microprocessor, Embedded System and IoT

Password: GR1234

CSE 423: Embedded System

Password: GR1234

CSE 426: Principles Of Robotics

Password: GR1234

CSE 313: Computer Network

Password: GR1234

5. Course Outlines (OBE format)

6. 100 marks assessment plan for any one of the courses

Methods of Assessment:

Direct:

1. Written exams.

2. Written homework problem sets/Quizzes.

3. Written reviews of scholarly articles.

4. Viva

Indirect:

1. Class participations

2. Peer-reviewed presentations

7. Pretest & Posttest Analysis

A pre-test is given to students at the beginning of a course to determine their initial understanding of the measures stated in the learning objectives, and post-test is conducted just after completion of the course to determine what the students have learned.

Pre-test and post-test was taken with same question. Each right answer carries 1 mark(s) & wrong answer carries -

0 mark(s). No mark will be deducted for unanswered

Sample question:

Part I. Multiple Choice questions.

1. Bayesian classifiers is

A) A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.

B) Any mechanism employed by a learning system to constrain the search space of a hypothesis.

C) An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.

D) None of these

2. Classification is –

A) A subdivision of a set of examples into a number of classes.

B) A measure of the accuracy, of the classification of a concept that is given by a certain theory.

C) The task of assigning a classification to a set of examples

D) None of these

3. Classification accuracy is

A) A subdivision of a set of examples into a number of classes

B) Measure of the accuracy, of the classification of a concept that is given by a certain theory.

C) The task of assigning a classification to a set of examples.

D) None of these

4. Cluster is

A) Group of similar objects that differ significantly from other objects

B) Operations on a database to transform or simplify data in order to prepare it for a machine-learning algorithm

C) Symbolic representation of facts or ideas from which information can potentially be extracted

D) None of these

5. Data mining is

A) The actual discovery phase of a knowledge discovery process

B) The stage of selecting the right data for a KDD process

C) A subject-oriented integrated time variant non-volatile collection of data in support of management

D) None of these

6. Inclusion dependencies

A) The amount of information with in data as opposed to the amount of redundancy or noise

B) One of the defining aspects of a data warehouse

C) Restriction that requires data in one column of a database table to a subset of another-column

D) None of these

7. Naive prediction is

A) A class of learning algorithms that try to derive a Prolog program from examples.

B) A table with n independent attributes can be seen as an n- dimensional space.

C) A prediction made using an extremely simple method, such as always predicting the same output.

D) None of these

8. Statistical significance is

A) The science of collecting, organizing, and applying numerical facts

B) Measure of the probability that a certain hypothesis is incorrect given certain observations.

C) One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational data

D) None of these

9. Which of the following is/are the Data mining tasks?

(a) Regression

(b) Classification

(d) All (a), (b), (c).

10. Which of the following is not a data mining functionality?

A) Characterization and Discrimination

B) Classification and regression

C) Selection and interpretation

D) Clustering and Analysis

Part II. Short Notes

1. What is Data Aggregation and Generalization?

2. What is OLAP?

3. What are the different functions of data mining?

4. What is meant by Multimedia mining?

Sample analysis of student learning:

Summary: Figure 1 and Figure 2 shows that large number of students obtained very poor marks in pre-test. This pre-test scores provide evidence which students get lower grades and those graphs actually demonstrated increased or decreased knowledge when answering the pre-test questions. We can say that completing the course resulted in improved performance on the pre-test, and make the inference that completing the course resulted in the desired expected learning.

Sample analysis of student learning on Post test: