FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL A Melding of Educational Strategies to Enhance the Introductory Programming Course Leo F. Denton, Dawn McKinney, and Michael V. Doran

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL CS1 Course: Introduction to Programming and Problem Solving Concepts Course format 4 credit hours 4 credit hours 15 week semester 15 week semester One 75-minute and three 50-minute sessions (or three 75 minute sessions) One 75-minute and three 50-minute sessions (or three 75 minute sessions) Integrated lecture and laboratory Integrated lecture and laboratoryTopics Problem solving strategies Problem solving strategies Programming concepts Programming concepts Internal representations of data Internal representations of data Control structures Control structures Use of IDE Use of IDE Methods Methods Arrays Arrays OOP basics. OOP basics.

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL 36688Paper View of several techniques described and studied separately in prior papers View of several techniques described and studied separately in prior papers Principal elements Principal elements Cognitive course framework Cognitive course framework Motivational strategies Motivational strategies Affective objectives Affective objectives Adjusting course content for novice learners Adjusting course content for novice learners Refining and organizing course content Refining and organizing course content

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Denton, L. F. and McKinney, D. “Affective Factors and Student Achievement: A Quantitative and Qualitative Study,” 34th ASEE/IEEE Frontiers in Education Conference, Savannah, GA, October 20 – 23, Denton, L. F., D. McKinney, and M. V. Doran. “Promoting Student Achievement With Integrated Affective Objectives,” American Society for Engineering Education Annual Conference & Exposition, Nashville, Tennessee, USA, Denton, L. F., M. V. Doran, and D. McKinney. “Integrated Use of Bloom and Maslow for Instructional Success in Technical and Scientific Fields,” in the Proceedings of the 2002 American Society for Engineering Education Annual Conference & Exposition, Montreal, Canada, Doran, M. V. and D. D. Langan. “A Cognitive-Based Approach to Introductory Computer Science Courses: Lessons Learned.” in the Proceedings of the 26th SISCSE Technical Symposium On Computer Science Education, March 1995, Nashville, TN, pp

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL McKinney, D. and Denton, L. F. “Affective Assessment of Team Skills in Agile CS1 Labs: The Good, the Bad, and the Ugly,” Proceedings of the 36th SISCSE Technical Symposium On Computer Science Education, St. Louis, MO, February McKinney, D. and Denton, L. F., “Houston, we have a problem: there’s a leak in the CS1 affective oxygen tank,” Proceedings of the 35th SISCSE Technical Symposium On Computer Science Education, March, Norfolk, VA, McKinney, D., Froeseth, J., Robertson, J., Denton, L. F., and Ensminger, D. “Agile CS1 Labs: eXtreme Programming Practices in an Introductory Programming Course,” Proceedings of XP/Agile Universe 2004.

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Principal Findings Course achievement correlates with affective factors Course achievement correlates with affective factors Student interest Student interest Belonging Belonging Effort Effort Affective factors often decrease during the semester Affective factors often decrease during the semester Sections using systematic affective objectives and strategies have higher levels of affective factors and higher course completion rates Sections using systematic affective objectives and strategies have higher levels of affective factors and higher course completion rates Affective factors impact all students including women and minorities Affective factors impact all students including women and minorities Internalization of professional practices can be accomplished in introductory courses and correlates with higher course grades Internalization of professional practices can be accomplished in introductory courses and correlates with higher course grades Lack of pressure Lack of pressure Perceived competence Perceived competence Value Value

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Principal Assessment Instruments Quantitative Quantitative Intrinsic Motivation Inventory (IMI) Intrinsic Motivation Inventory (IMI) Institutional Integration Scale Institutional Integration Scale Anderson-Butcher Belonging Scale Anderson-Butcher Belonging Scale Qualitative Qualitative Comparative-reflective surveys Comparative-reflective surveys Peer Evaluations Peer Evaluations BAM chart BAM chart

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Bloom-based Cognitive Framework Levels: Knowledge Knowledge Comprehension Comprehension Application Application Analysis Analysis Synthesis Synthesis Evaluation EvaluationBenefits: Standards-based approach Standards-based approach Clear expectations Clear expectations Transferability Transferability Content-centered Content-centered

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Something’s Amiss … Overall results Overall results Low course completion rates Low course completion rates Low student satisfaction Low student satisfaction Three types of students Three types of students Non-achievers - students not meeting course objectives Non-achievers - students not meeting course objectives Survivors - passed with significant frustrations and low motivation Survivors - passed with significant frustrations and low motivation Excellers - achieved cognitively, were motivated, and internalized course objectives Excellers - achieved cognitively, were motivated, and internalized course objectives

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Obstacles to Achievement, Retention, and Recruitment Non-sustained student interest Non-sustained student interest Inadequate faculty and peer support Inadequate faculty and peer support Inadequate prior knowledge Inadequate prior knowledge Attraction of other disciplines Attraction of other disciplines Intimidating atmosphere Intimidating atmosphere Difficulty of discipline Difficulty of discipline Poor teaching Poor teaching Large class sizes Large class sizes Personal problems Personal problems

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Motivation Impacts physical process of learning in the brain Impacts physical process of learning in the brain Promotes individual growth Promotes individual growth Increases group effectiveness Increases group effectiveness Leads to higher time-on- task and overall learning Leads to higher time-on- task and overall learning

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Motivational Strategies Commitments to quality Commitments to quality Discussion approach Discussion approach Most desired qualities from the National Association of Colleges and Employers Most desired qualities from the National Association of Colleges and Employers Armstrong – each person’s potential for genius Armstrong – each person’s potential for genius Helen Keller – persistence and promise Helen Keller – persistence and promise Polya, Maslow, Krathwohl Polya, Maslow, Krathwohl Reflection approach Reflection approach Goal-setting Goal-setting Time management Time management Self-regulation Self-regulation

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL BAM Chart

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Krathwohl-based Affective Framework Benefits Standards-based approach Standards-based approach Transition at-risk students to excellers Transition at-risk students to excellers Achieve valuing rather than compliance Achieve valuing rather than compliance Enhance personal identification with discipline Enhance personal identification with discipline Transferability Transferability Learner-centered Learner-centered Levels Receiving Receiving Responding Responding Valuing Valuing Organization Organization Characterization Characterization

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Examples of Affective Objectives Receiving: Students come to class ready and willing to program Receiving: Students come to class ready and willing to program Responding: Students turn in assignments that follow coding and documentation standards of the class Responding: Students turn in assignments that follow coding and documentation standards of the class Valuing: Valuing: Students recommend the use of Polya’s problem-solving strategy to fellow classmates who are having difficulty solving a problem. Students value the efficiency that can be gained from effective algorithms, data structures such as arrays, and problem-solving techniques. Students prefer to use arrays to solve problems rather than using non-aggregate data items when appropriate. Organization: Students develop habits of reflective problem solving as it relates to developing software Organization: Students develop habits of reflective problem solving as it relates to developing software

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL The Intellectual Challenge Remains Mostly first time programmers and a few experienced hackers Mostly first time programmers and a few experienced hackers Instructors have expert tacit knowledge that is not easily decomposed into distinct Instructors have expert tacit knowledge that is not easily decomposed into distinct Computational concepts Computational concepts Programming language syntax Programming language syntax Problem solving methodologies Problem solving methodologies

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Moving Novices Toward Expert Understanding Soloway’s methodology Soloway’s methodology Explore and evaluate multiple data representation Explore and evaluate multiple data representation Explore and evaluate multiple problem decompositions Explore and evaluate multiple problem decompositions Select and compose a particular solution Select and compose a particular solution Implement solution Implement solution Reflect on the solution and the process Reflect on the solution and the process Minimizing cognitive overload Minimizing cognitive overload Zone of proximate development – Vygotsky Zone of proximate development – Vygotsky Spiral coverage – Bruner Spiral coverage – Bruner Subsumption learning – Ausebel Subsumption learning – Ausebel Treat computational concepts, syntax, and problem-solving dimensions separately even when there is overlap Treat computational concepts, syntax, and problem-solving dimensions separately even when there is overlap

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Organizing and Refining Content Instructional templates Instructional templates Whitehead’s rhythm of education Whitehead’s rhythm of education Keller’s ARCS model Keller’s ARCS model Gagné’s nine events of instruction Gagné’s nine events of instruction Support for various learning styles Support for various learning styles Relevant content Relevant content Interesting Interesting Related to professional development Related to professional development Feedback from students Feedback from students

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Whitehead’s Rhythms of Education Cyclical Periods of Learning Romance period Romance period Fascination with the broad significance of the idea Fascination with the broad significance of the idea Motivation to actively pursue the more rigorous learning Motivation to actively pursue the more rigorous learning Precision period Precision period Mastery of data collection techniques, notations, procedures Mastery of data collection techniques, notations, procedures Development of relevant problem-solving strategies Development of relevant problem-solving strategies Near transfer Near transfer Generalization Generalization Realized patterns, meaning, and general applications Realized patterns, meaning, and general applications Understanding of the worth of the learning Understanding of the worth of the learning Far transfer Far transfer

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Keller’s ARCS Model Attention Attention Incongruity Incongruity Inquiry/participation Inquiry/participation Concreteness Concreteness Humor Humor Relevance Relevance Experience / modeling Experience / modeling Present / future worth Present / future worth Power / affiliation / achievement perspectives Power / affiliation / achievement perspectives Needs matching Needs matching Confidence Confidence Organization of content Organization of content Clear requirements Clear requirements Positive attributions Positive attributions Choice Choice Satisfaction Satisfaction Natural and unexpected rewards Natural and unexpected rewards

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Gagné’s Nine Events of Instruction Gain attention Gain attention Inform learner of objectives Inform learner of objectives Stimulate recall of prior learning Stimulate recall of prior learning Present content Present content Provide guidance to learners Provide guidance to learners Get the learners to practice / perform Get the learners to practice / perform Provide feedback Provide feedback Assess learners Assess learners Enhance retention of what was learned and transfer Enhance retention of what was learned and transfer

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Balance Teaching To Match Multiple Learning Styles

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Concept Map Example

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Recap and Concluding Remarks Principal elements of whole Principal elements of whole Cognitive course framework Cognitive course framework Motivational strategies Motivational strategies Affective objectives Affective objectives Adjusting course content for novice learners Adjusting course content for novice learners Refining and organizing course content Refining and organizing course content Incremental implementation Incremental implementation Positive faculty cross-training and development Positive faculty cross-training and development Course completion rates Course completion rates

FIE 2005 Indianapolis, Indiana School of Computer and Information Sciences University of South Alabama, Mobile, AL Leo F. Denton Dawn McKinney Michael V. Doran