We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byJacob Daley
Modified over 3 years ago
TEMPLATE DESIGN © 2008 www.PosterPresentations.com Novice Programmer Planning: A Grounded Theory Approach Jonathan Wellons, Dr. Julie Johnson Dr. Renee McCauley Vanderbilt University College of Charleston Context: Planning in Software Design Grounded Theory Organization of DataResults: Hypotheses Our goal in grounded theory is to creatively examine the apparent connections between categories to suggest theories which can explain the data before us. Two families of theories suggest themselves: Those that suggest a students background or personal details are correlated with a tendency to plan and those that appear to show a higher quality of educational experience for students who plan. Beginning with the first family, the Quantitative Background category appears to be well-correlated with a students propensity to plan. In particular, enjoyment of Mathematics is a better predictor than the students claimed skill. Therefore we formulate: Theory I: Students with enjoy Mathematics show a greater tendency to plan their programs A similar correlation can be seen in subjects who enjoy pastimes, such as logic and strategy games and scientific reading. Theory II: Students with quantitative hobbies are more likely to plan their programs The second family of theories is strongly represented in the Goal Setting and Achievement category. In addition to expressing their ambition in other ways, students may choose between alternatives that offer different numbers of points in several of the programming assignments in the class. Based on this, we formulate: Theory III: Students who plan their programs have higher ambitions in the assignment Based on students descriptions and assessments of their goals (such as grades, time taken, material mastered), we formulate: Theory IV: Students who plan their programs are more likely to achieve their goals Finally, based on wide ranging comments about the quality of a students learning experience, we formulate Theory V: Students who plan programs are more likely to report a positive experience in the class What is Versus What works Practitioners divide Research on Teaching into two kinds: What is and What works. What is research focuses on observations about current conditions and processes in the learning environment. What works research tests and measures alternative teaching practices. This is a What is project attempting to suggest causes and effects of student program planning for further study. Study Design Conclusion and Future Work In order to gather accurate data, volunteers were anonymously interviewed. This year the ES140 class was divided into three successive sections, each lasting approximately 14 lecture periods or 4 weeks. Each section consists of 25-30 students and the same subject material is taught to each section. Members of each section were interviewed. This course introduces the fundamentals of programming (using MATLAB) within the context of cryptography. The general idea behind the course is to familiarize freshman engineering students with problem solving techniques and tools (such as MATLAB and Excel) while giving them a snapshot of the field of Computer Science and some of its practical applications. All interviews are voice recorded for later analysis. Volunteers are encouraged to describe their experiences during several assignments, what type of plan they created, how detailed it was, and how it was adapted. Subjects are questioned about their background in programming, their hobbies and other details that could lead to a planning theory. Jonathan Wellons gathered the data, held office hours and guest lectured in 2 or 3 class sessions of each of the 3 sections to develop a rapport with students and introduce the study. Grounded theory is a form of qualitative research based on the formation of theory from data. The theory that results may be tested in subsequent quantitative research. This methodology consists of 5 steps: 1) gathering of data, 2) isolating meaningful points, or codes, 3) grouping of codes into concepts, 4) constructing categories that highlight the relationship between concepts, including a core category and 5) the suggestion of one or more theories which explain the data and may be tested in future research. Grounded theory researchers begin by casting a wide net to capture a diverse and multi-dimensional set of data which may be fertile ground for theories. Grounded theory is Emergent Research rather than Hypothesis- Testing Research. Our goal is to generate ideas to be rigorously tested, without preexisting bias or theory. We asked open-ended questions and subjects were encouraged to discuss any aspects of their programming experience that they deemed meaningful. In this project, we have applied grounded theory to interviews with novice programmers about their first programs. Through the principles of grounded theory (which leaves the testing of the theories for further work), we have coded, conceptualized and categorized the interview data. Next we have elucidated the connections between categories to generate several plausible theories to explain the data. Five theories are proposed in this project, based on the observed correlation between certain types of personal background and regular activities with the tendency to plan and also on the correlation of planning to several metrics of a successful educational experience. Future work consists of two kinds: testing and refinement of these theories and the development of What works projects to realize the benefits proposed by these theories. Insights in to how students plan will enable us to offer guidance for students in these critical, early programming assignments. Next fall, we will proceed with a What works project with measurable objectives to test teaching strategies intended to exploit the apparent advantages suggested by theories III, IV and V. Example CodeConcept Initial plan was a short, vague list of tasksInitial Planning Plan failed because MATLAB doesnt handle long strings Plan Adequacy Program was constructed of fragments adapted from in-class examples Programming Searched for help on Google, e.g., how to write a for loop Help Sources Wrote program in 3-4 parts which were tested separately and combined at the end Testing My goal was to receive an A or BGoals It took 2 to 2.5 hours to finish. Had thought it would take 1 hour Time Needed First day was too much, second day I started to understand, it clicked on the third day Class Experience Programming background consists of using the Starcraft map editor Programming Background Had some advanced Math, not good at it but loves it Quantitative Background Calls self a number cruncher in everyday lifeHobbies Why go for the extra credit when I dont understand the basics Ambition Rather than write a time-consuming brute-force program, solved the permutation puzzle visually out of 362,880 possibilities Lateral Thinking Influenced lab partners to use pseudocode in the future Personality ConceptsCategory Initial Planning, Plan AdequacyPlanning Programming, Help Sources, TestingProgramming Methodology Goals, Time Needed, Class ExperienceGoal Setting and Achievement Programming Background, Quantitative Background Background Hobbies, Ambition, Lateral Thinking, PersonalityPersonal Our raw data consists of 11 interviews with students in Engineering Science 140. The interviews total length is approximately 50,000 words. The interviews were refined into approximately 400 codes, divided over 14 concepts. The table below shows example codes taken from the data along with the corresponding concept. The codes are data points, which in the context of the interview, reveal a possible connection to planning. Similar concepts are clustered into categories. The 14 categories that naturally arose in the above table are shown with their category in the table below. The Planning category is naturally the best choice to be deemed the core category as it is the focus of our work. Relationships between this category and others gives rise to theories. Programming classes differ in their emphasis on planning. Those that encourage it often do so in very different forms (e.g., writing the program comments in advance of any code versus a top- down modularization). It is widely known that planning is critical to programming success for non-trivial projects and that students may experience deceptive initial success with unplanned small projects. Given that programming is an essential skill in many engineering disciplines and that an enormous diversity of educational approaches are employed, it is natural to ask if there is a better way to teach planning. Kuhl and Goschke proposed a model for self-regulated learning which includes the steps of goal setting and planning. They believed that the model was recursive and that learners would return to this task again and again as they experienced internal feedback from the products they generated. We proposed this study in order to observe and explain planning in a broader sense among novice programmers. Due to the relative lack of literature in the field, we believe it would be helpful to analyze qualitative data to suggest a set of emergent theories. Such a theory and the subsequent results of its testing (not a part of this study) could lead to the development of tools to help students improve and refine this critical self-regulatory skill. Our central question is: What theories can we formulate that could ultimately lead to better teaching practices in program planning? Limitations Further Information  Kuhl, J., & Goschke, T. (1994). A theory of action control: Mental subsystems, modes of control, and volitional conflict-resolution strategies. In J. Kuhl & J. Beckmann (Eds.), Volition and personality Seattle, WA: Hogrefe & Huber.  Martin, Patricia Yancey, & Turner, Barry A.. (1986). Grounded Theory and Organizational Research. The Journal of Applied Behavioral Science, 22(2), 141.  Creswell, John W. (2006). Qualitative Inquiry and Research Design. Thousand Oaks, CA: Sage. I gratefully acknowledge the support of Dr. Johnson, Derek Bruff, the CFT and the Center for Integration of Research, Teaching and Learning project (NSF Grant No. DUE-0717768). Without this support, this research would not have been possible. The main limitation of qualitative research of this kind is the time required to code and understand the large volume of interview data. It is not clear if more codes or a greater diversity of data sources sould be helpful. The nature of grounded theory places the burden testing the suggested theories on later work. Thus, we naturally cannot say whether the theories are true, which direction of cause and effect is present (if either), the strength of the correlation or how the theories could immediately be useful. Further quantitative research is required with statistical backing. Because the qualitative data gathered has an unlimited number of dimensions and is subject to interpretation, the choice of emergent theories is, by nature, subjective.
Introduction to Earth Science Section 2 Section 2: Science as a Process Preview Key Ideas Behavior of Natural Systems Scientific Methods Scientific Measurements.
Section 2: Science as a Process
CHAPTER 1 Understanding RESEARCH
An Introduction to Research Methodology
Peer Assessment of Oral Presentations Kevin Yee Faculty Center for Teaching & Learning, University of Central Florida Research Question For oral presentations,
Science as a Process Chapter 1 Section 2.
It really made me think about my own future: The potential benefits of an academically-orientated undergraduate careers module John Arnold & Laurie Cohen.
1 Science as a Process Chapter 1 Section 2. 2 Objectives Explain how science is different from other forms of human endeavor. Identify the steps that.
QUANTITATIVE RESEARCH Presented by SANIA IQBAL M.Ed Course Instructor SIR RASOOL BUKSH RAISANI.
Alternative Strategies for Evaluating Teaching How many have used end-of-semester student evaluations? How many have used an alternative approach? My comments.
TEA Science Workshop #3 October 1, 2012 Kim Lott Utah State University.
Analytical methods for Information Systems Professionals Week 13 Lecture 1 CONCLUSION.
Research Methods in Crime and Justice
Development of Research Methodologies in Various Disciplines By Dr Ranu Varshney & Mrs. Nisha Chaturbedi.
Week 2 The lecture for this week is designed to provide students with a general overview of 1) quantitative/qualitative research strategies and 2) 21st.
Institute of Professional Studies School of Research and Graduate Studies Introduction to Business and Management Research Lecture One (1)
Professor Sanjoy Bandopadhya Ustad Alauddin Khan Professor of Instrumental Music Rabindra Bharati University, Kolkata.
ASSESSMENT OF CORE SKILLS/ GENERAL EDUCATION OUTCOMES Angelina Hill, PhD Associate Director, Office of Academic Assessment.
Student Learning Outcomes Assessment
Curriculum Development and Course Design
Data Analysis, Interpretation, and Reporting
Qualitative Research January 19, Selecting A Topic Trying to be original while balancing need to be realistic—so you can master a reasonable amount.
ScWk 242 Course Overview and Review of ScWk 240 Concepts ScWk 242 Session 1 Slides.
Discuss how researchers analyze data obtained in observational research.
RESEARCH IN MATH EDUCATION-3
Intelligence Give a definition of intelligence that you could defend, explaining why you believe you could defend it. Give examples of ways your definition.
The Next Generation Science Standards: 4. Science and Engineering Practices Professor Michael Wysession Department of Earth and Planetary Sciences Washington.
Chapter 6: Analyzing and Interpreting Quantitative Data
Inquiry Primer Version 1.0 Part 4: Scientific Inquiry.
School Counselors Doing Action Research Jay Carey and Carey Dimmitt Center for School Counseling Outcome Research UMass Amherst CT Guidance Leaders March.
VARIETY VERSUS FAMILIARITY IN ASSESSMENT TASK TYPE IN HIGHER EDUCATION Neil Currant.
WHAT IS THE NATURE OF SCIENCE?. SCIENTIFIC WORLD VIEW 1.The Universe Is Understandable. 2.The Universe Is a Vast Single System In Which the Basic Rules.
MGT-491 QUANTITATIVE ANALYSIS AND RESEARCH FOR MANAGEMENT OSMAN BIN SAIF.
Critical Reading Strategies: Overview of Research Process
How to write a publishable qualitative article
Ability-Based Education at Alverno College. Proposed Outcomes for Session 1. To introduce you to Alvernos approach to designing integrative general education.
Course Certification Process General Education Board
Specifying a Purpose, Research Questions or Hypothesis
College Expectations with High School Skills: What is a well-educated high school graduate? Texas High School Project.
SIMAD University Formulating the Research Design Ali Yassin Sheikh.
1 Embedded Math as an Effective Tool for Smooth Transition from High School into Integrated Engineering: Teacher and E Centered Learning Riadh W. Y.
T 7.0 Chapter 7: Questioning for Inquiry Chapter 7: Questioning for Inquiry Central concepts: Questioning stimulates and guides inquiry Teachers use.
Critical Thinking in Education. Defining Critical Thinking Asking pertinent questions Evaluates statements & arguments Admits a lack of knowledge & understanding.
Sabine Mendes Lima Moura Issues in Research Methodology PUC – November 2014.
Student Centered Teaching Through Universal Instructional Design Part II.
RESEARCH METHODOLOGY Research and Development Research Approach Research Methodology Research Objectives Engr. Hassan Mehmood Khan.
1 Summer 2012 Educator Effectiveness Academies English Language Arts Transitioning to the CCSS by Making Strategic and Informed Choices in the Classroom.
For science courses, exams determine the majority of the student grades. Testing students on their comprehension and application of the course material.
Nursing research Is a systematic inquiry into a subject that uses various approach quantitative and qualitative methods) to answer questions and solve.
© 2017 SlidePlayer.com Inc. All rights reserved.