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An Empirical Study of In- Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science North Carolina State University ICER 2015

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Page 1: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

An Empirical Study of In-Class Labs on Student Learning of Linear Data StructuresSarah HeckmanTeaching Associate ProfessorDepartment of Computer ScienceNorth Carolina State University

ICER 2015

Page 2: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Problem

ICER 2015

CSC116 CSC216 CSC316

• 7-8 sections• 33 students• 1 instructor• 2 TAs• Lecture/Lab

• 1-2 sections• 70-90 students• 1 instructor• 2-3 TAs• Lecture

• 1-2 sections• 70-90 students• 1 instructor• 2-3 TAs• Lecture

Transition!

Retention!

Lab? In-Class Labs?Do Nothing?

Page 3: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Research Goal

• To increase student learning and engagement through in-class laboratories on linear data structures

• Hypothesis: active learning practices that involve larger problems would increase student learning and engagement

ICER 2015

In-class Labs > Pair & Share

Page 4: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Research Questions

• Do in-class laboratories on linear data structures increase student learning on linear data structures exam questions when compared to active-learning lectures?

• Do in-class laboratories on linear data structures increase student engagement on linear data structures exam questions when compared to active-learning lectures?

ICER 2015

Page 5: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Active Learning in CSC216• “engaging the students in the process of learning

through activities and/or discussion in class, as opposed to passively listening to an expert” [Freeman, et al. 2014]

• Control: Active Learning Lectures – 2-5 pair & share exercises per class– Submitted through Google forms

• Treatment: In-class Labs– Lab activity for the entire lecture period– Pre-class videos introduced topic

ICER 2015

Page 6: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Study Participants

Metric Section 001 Section 002# Enrolled 85 102Participants (completed course)

49 60

Dropped/Withdrawn (consenting only)

3 4

Women 9 10Meeting Time TH 2:20-3:35p MW 2:20-3:35p

ICER 2015

• Self-selected into section during standard registration period• Populations were similar as measured by a survey on

experience with tooling and self-efficacy.

Page 7: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Methods• Quasi-Experimental

– Counter-balanced design– Learning measured through exams– Engagement measured through observations of class

meetings

ICER 2015

Lists

Array Array

Array Array Linked Linked

Linked Linked

Iterators

Exam 1

001

002

Replication Materials:http://people.engr.ncsu.edu/sesmith5/216-labs/csc216_labs.html

Replication Materials:http://people.engr.ncsu.edu/sesmith5/216-labs/csc216_labs.html

Observed Class Meetings

Page 8: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Student Learning – Exam 1

• Part 4: Method Tracing with ArrayLists• Part 5: Writing an ArrayList method

ICER 2015

Item Points S001 Mean

S001 SD

S002 Mean

S002 SD

p-value

E1 P4#8 5 3.63 1.56 4.35 1.45 < 0.010

E1 P4#9 5 4.18 1.07 4.57 1.09 0.016

E1 P4#10 5 2.63 2.40 3.45 2.18 0.149

E1 P4 15 10.45 3.97 12.37 3.74 < 0.010

E1 P5 20 17.76 4.0 18.25 4.09 0.233

Page 9: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Student Learning – Exam 2

• Part 3 – Linked Node Transformation• Part 5 – Writing a LinkedList Method

ICER 2015

Item Points S001 Mean

S001 SD

S002 Mean

S002 SD

p-value

E2 P3 16 9.43 5.85 11.80 6.41 < 0.010

E2 P5 20 11.80 4.14 12.58 4.21 0.412

Page 10: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Student Learning – Exam 3

• Comprehensive 3 hour final exam• Stack Using an ArrayList• Queue Using a LinkedList

ICER 2015

Item Points S001 Mean

S001 SD

S002 Mean

S002 SD

p-value

E3 Array 10 8.31 2.45 8.46 2.49 0.313

E3 Linked 10 8.36 2.53 881 2.45 0.221

E3 Score 105 85.02 29.17 87.23 28.92 0.372

Page 11: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Student Engagement

• Observations for ArrayList and LinkedList class meetings

• Observers were graduate students and a colleague participating in a Teaching and Learning seminar

• Counts of students off topic during lecture and exercise portions of the class

• Some inconsistent use of the observation protocol

ICER 2015

Page 12: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Student Engagement

ICER 2015

Observation Class Type

# Off Topic – Lecture

# Off Topic – Exercise

Questions of Teaching Staff

1 Lab 5 7 32

2 Lecture 62 49 12

3 Lab 10 43 50

4 Lecture 46 16 ---

5 Lecture --- --- ---

6 Lab 5 10 33

7 Lecture 52 54 2

8 Lab 16 5 ---

Lab Average 9 16.3 38.3

Lecture Average 53.3 39.7 7

Lecture / Lab 5.9 2.4 0.2

Page 13: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Threats to Validity

• External Validity– Two sections of the same course, taught by the same

instructor, in the same semester, and same time of day

– Replication needed in other contexts to generalize further

– Could provide additional data points in future meta-analyses

ICER 2015

Page 14: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Threats to Validity• Internal Validity

– Selection bias: students selected their own sections• Initial surveys shows groups were similar

– Confounding factors• Materials shared between groups• Effect size – only 6 in-class labs

– Differential Attrition Bias• Considered “soft-drops” in the study

– Experimenter Bias• Participants were not revealed until after the semester was over

ICER 2015

Page 15: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Threats to Validity

• Construct Validity– Exams as Measures of Learning

• Exam 1 and Exam 2 were similar, but not the same, between sections

• Exam 3 was common• Does exam really measure student learning?

– Survey• Wording may be confusing for prior tool experience• Efficacy questions not a validated instrument

– Observation Protocol as Measure of Engagement• Inconsistent use by observers

ICER 2015

Page 16: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Discussion• Did in-class labs increase student learning?

– No, at least not as measured by exam questions– Both control and intervention were active learning

• Maybe a simple active learning intervention is enough– Comparisons with earlier semesters may show more

• Did in-class labs increase student engagement?– Yes and No– The atmosphere in the classroom was fantastic– But many questions were technology and not concept

• Completion – 72% of students earned a C or higher– Not reaching the higher levels of completion we expect from active

learning literature

ICER 2015

Page 17: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Future Work• Additional Work on Fall 2014 Data

– Compare results on final exam with previous courses– Incorporate analysis of other measures of learning –

projects, exercises, etc.• Starting in Fall 2015

– Additional in-class labs → Lab-based course– Measure types of questions asked during in-class labs– Use labs as a way to encourage best practices

(frequent commits to version control, TDD)

ICER 2015

Replication Materials:http://people.engr.ncsu.edu/sesmith5/216-labs/csc216_labs.html

Replication Materials:http://people.engr.ncsu.edu/sesmith5/216-labs/csc216_labs.html

Page 18: An Empirical Study of In-Class Labs on Student Learning of Linear Data Structures Sarah Heckman Teaching Associate Professor Department of Computer Science

Thank You!

Questions?Comments?Concerns?

Suggestions?

ICER 2015