1. PILeT: Python Interactive Learning Tool
Presented by:
Bedour Alshaigy
Supervisory team:
Dr.Samia Kamal
Dr.Faye Mitchel
Dr.Clare Martin

2. Introduction
With to the rapid growth of internet technologies and its application, there has been an exploding demand in the industry for graduates with computing expertise.
However, introductory programming courses are currently facing incremental dropout and failure rates by 40% at university level [1] especially after completing the first year [2].
1. Manaris, 2007, Dropping CS enrollments: or the emperor's new clothes?
2. Beaubouef, 2005, Why the high attrition rate for computer science students: some thoughts and observations.

4. Programming Problems
Although several factors have been attributed to students’ failure to program we are far from fully understanding the underlying reasons behind different progression rates amongst them.
The problems include:
Understanding the syntax and semantics of a programming language.
Challenging programming concepts.
Development of programming misconceptions.
Code reading and tracing.
Debugging and error finding.

5. Program Comprehension
Programming Problems
Authors
Mental Model
Learning Style
Teaching Methodology
Programming Language
Program Comprehension
Programming Concepts
Cognitive Skills
(Allert, 2004) x
(Beaubouef and Mason, 2005)  x
(Lahtinen et al., 2005)
(McGettrick et al., 2005)
(Milne and Rowe, 2002)
(Robins et al., 2003)
(Tan et al., 2009)
(Zander et al., 2009)
No. Times Outlined 3 6 5 4

6. Learning Styles
The Felder-Silverman Learning Style Model (Felder and Silverman, 1988) classifies learners as:
Sensing: practical prefers concrete facts.
Intuitive: innovative and concerned with theories.

7. Teaching Methodologies

8. Why Python? Expressive syntax and meaningful semantics.
User friendly error feedback and powerful debugging tools.
Produces visually appealing GUI (graphical user interface) components.
Facilitates easy transition to different programming paradigms and computer science modules.

9. PILeT

10. Experiment Control group 19 CS Circles 19 PILeT 19
The experiment lasted 75 minutes followed by a quiz consisting of 3 programming questions.
Concepts tested:
Selection statements and for loop.

11. Results

12. Threats to Validity Learning styles is pseudoscience.
Learning styles are based on self reported questionnaires.
Learning styles are subject to change.
It is expensive to customise instructions based on learning styles.
The problem is not programming, it is problem solving.
1. The headline is a blanket statement dismissive of the tons of evidence on the existence of learning styles (don't take my word for it, just type learning styles in Google Scholar). 2. The literature identifies 71 learning models so far, did they go through each model and examine the evidence resulting from these models? If so where are the findings? 3. "There is no coherent framework". While some of the models are questionable, or based on on a self-report questionnaire, or even designed in such a way to match the findings, does that deny the existence of learning styles? Let's strip it to its simplest form, suppose a student expressed difficulties learning programming from a textbook, and they identify themselves as a "visual learner", regardless of whether their diagnosis is correct, should the instructor reject the diagnosis and insist on the student learning from a textbook instead of offering visual instructions such as videos or code visualisation? 4. "Categorising individuals can lead to the assumption of fixed or rigid learning style". Several models believe that learning styles are fluid and develop overtime due to the teaching environment or other aspects. The Felder Silverman model does not pigeonhole learners into categories but instead states that students have a tendency towards a style more than the other depending on the concept they are learning or even the subject. 5. Though customising the material based on learning style is costly, it's a one-off cost. That is not to say that instructors should spend their time figuring out each learning style in existence and produce appropriate material accordingly, but having one or two alternatives options is beneficial to many students. This also calls for the need for open educational resources. 6. Let us harness the power of technology! IBM is currently working on adopting cloud-based cognitive systems to collect and analyse student data over a period of time to provide personalised learning experiences for each of them.

13. Thank you!

14. Questions?