CV in DS: Evaluation of Effectiveness

1. CV in DS: Evaluation of Effectiveness Sudeep Sarkar Computer Science and Engg. University of South Florida, Tampa

2. The Need… • Need for expertise in handling and manipulating image data • Need for deeper incorporation of Image ADT and associated algorithms into undergraduate curriculum. • Some of the core concepts can be effectively illustrated using image manipulations.

3. Some Other Ways… • Using visualizations to enhance DS • Michail (1996) • Shaffer et al. (1996) • Whale (1994) • Augmentation by a laboratory course • Mahanti (1997) • Using multiple visual representations • Hancilles (1997) • Hands on approach (e.g. sorting) • Geller (1998)

4. Our Approach… • Enhance existing DS courses using image-based assignments and examples. • We present some evaluation of this strategy • Hypothesis: Image-based programming assignments improve the understanding of standard data structure concepts.

5. Structure of the DS Course • Lower-level introductory course • Pre-reqs: basic knowledge of programming constructs. They may have some introductory knowledge about linked lists. • Assignments in C and Unix based. • 24 students • Low-overhead in terms of class time • Just one lecture spent on introducing image related concepts.

6. Data Collection: First Source • Performance of pre- and post-tests • Three assignments • Information hiding, modularity, ADTs • Stacks, queues, list of lists in depth-first and breadth-first searches • Graph ADT and connected component labeling • Control: concept of binary trees • Questions in quizes and examinations tested the assignment materials.

7. Data Collection: Second Source • Performance in the assignments evaluated using a detailed grading rubric • Documentation • Source code • Actual program run • The students were provided with the detailed grading rubric at the beginning of the semester.

8. Data Collection: Third Source • Student evaluation of the assignments. • # of hours spent on the assignments • Sufficiency of image-related materials • Would they recommend the course to others? • How much the assignments enhanced their understanding of various DS concepts. A list of topics was provided.

9. Content of the Assignments • Context: segmenting walking persons in motion sequences • Frame differencing • Morphological opening and closing • Connected component labeling • Proximity based grouping of the regions • Assignments were woven together to result in one coherent project.

10. The Assignments

11. Additional Concepts Emphasized • Information hiding • Use of DS is shielded from implementation • Software reuse • Code management using Makefiles • Modularity

12. Pre- and Post Test Scores

13. ANOVA of Pre- & Post Scores

14. Pre- & Post- Differences

15. Assignment Grades • Source code was of high quality • Reports could have been better • Performance became better • Interest did not fall

16. Student Evaluations • Hours per assignment: 48.6 hours • Image related support materials adequate • Would you recommend the course? • Simply recommend to highly recommend • Improved significantly to greatly improved the understanding of various DS concepts

17. Perception of Students • Image related assignments provided a real world context • One repeat student found the image-related examples better than the “game of life” example in the book. • Assignments were demanding, but most of the students liked the challenge.

18. In Conclusion… • Some effort upfront in preparing the assignment and background materials • The ones we used are available on the net. • http://marathon.csee.usf.edu/~sarkar/ds_ip.html • Students were more engaged when image-based problems were worked out • Nice contexts for active learning • Potential for attracting bright student into CV