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Markman, A. B. and Gentner, D. (2001). Thinking. Annual Review of Psychology, 52:223–247. Marton, F. (1986). Phenomenography – A Research Approach to Investigating Different Understandings of Reality. Journal of Thought, 21(3):28–49. Marton, F. (1993). Our Experience of the Physical World. Cognition and Instruction, 10(2):227–237. Marton, F. (2000). The Structure of Awareness. In: J. A. Bowden and E. Walsh (eds.), Phenomenography, pp. 102–116. RMIT University Press. Marton, F. (2007). Towards a Pedagogical Theory of Learning. In: N. Entwistle and P. Tomlinson (eds.), Student Learning and University Teaching, pp. 19–30. British Psychological Society. Marton, F. and Booth, S. (1997). Learning and Awareness. Lawrence Erlbaum. Marton, F. and Pong, W. Y. (2005). On the Unit of Description in Phenomenography. Higher Education Research & Development, 24(4):335–348. Marton, F., Runesson, U., and Tsui, A. B. M. (2004). The Space of Learning. In: F. Marton and A. B. M. Tsui (eds.), Classroom Discourse and the Space of Learning, pp. 3–40. Lawrence Erlbaum. Marton, F. and Säljö, R. (1976). On Qualitative Differences in Learning: I – Outcome and Process. British Journal of Educational Psychology, 46(1):4–11. Marton, F. and Tsui, A. B. M. (eds.) (2004). Classroom Discourse and the Space of Learning. Lawrence Erlbaum. Mason, R. and Cooper, G. (2012). Why the Bottom 10% Just Can’t Do It – Mental Effort Measures and Implication for Introductory Programming Courses. In: M. de Raadt and A. Carbone (eds.), Proceedings of the 14th Australasian Conference on Computing Education (ACE ’12), volume 123 of CRPIT, pp. 187–196. Australian Computer Society. Matthews, M. R. (1992). Old Wine in New Bottles: A Problem with Constructivist Epistemology. In: H. Alexander (ed.), Proceedings of the 48th Annual Philosophy of Education Meeting, pp. 303–311. Philosophy of Education Society. Matthews, M. R. (1994). Science Teaching: The Role of History and Philosophy of Science. Routledge. Matthews, M. R. (2000). Appraising Constructivism in Science and Mathematics. In: D. C. Phillips (ed.), Constructivism in Education, pp. 161–192. The National Society For The Study Of Education. Mayer, R. E. (1975). Different Problem-Solving Competencies Established in Learning Computer Programming With and Without Meaningful Models. Journal of Educational Psychology, 67(6):725– 734. Mayer, R. E. (1979). A Psychology of Learning BASIC. Communications of the ACM, 22(11):589–593. Mayer, R. E. (1981). The Psychology of How Novices Learn Computer Programming. ACM Computing Surveys, 13(1):121–141. Mayer, R. E. (1985). Learning in Complex Domains: A Cognitive Analysis of Computer Programming. In: The Psychology of Learning and Motivation: Advances in Research and Theory, volume 19, pp. 89–130. Academic Press. Mayer, R. E. (2004). Should There Be a Three-Strikes Rule Against Pure Discovery Learning? American Psychologist, 59(1):14–19. Mayer, R. E. (ed.) (2005). The Cambridge Handbook of Multimedia Learning. Cambridge University Press. Mayer, R. E. (2009). Multimedia Learning. Cambridge University Press, 2nd edition. 404
Mayer, R. E. and Alexander, P. A. (2011). Handbook of Research on Learning and Instruction. Routledge. McCartney, R., Boustedt, J., Eckerdal, A., Moström, J. E., Sanders, K., Thomas, L., and Zander, C. (2009). Liminal Spaces and Learning Computing. European Journal of Engineering Education, 34(4):383–391. McCauley, R., Fitzgerald, S., Lewandowski, G., Murphy, L., Simon, B., Thomas, L., and Zander, C. (2008). Debugging: A Review of the Literature from an Educational Perspective. Computer Science Education, 18(2):67–92. McCracken, M., Almstrum, V., Diaz, D., Guzdial, M., Hagan, D., Ben-David Kolikant, Y., Laxer, C., Thomas, L., Utting, I., and Wilusz, T. (2001). A Multi-National, Multi-Institutional Study of Assessment of Programming Skills of First-Year CS Students. SIGCSE Bulletin, 33(4):125–180. McCune, V. and Hounsell, D. (2005). The Development of Students’ Ways of Thinking and Practising in Three Final-Year Biology Courses. Higher Education, 49(3):255–289. McKeithen, K. B., Reitman, J. S., Rueter, H. H., and Hirtle, S. C. (1981). Knowledge Organization and Skill Differences in Computer Programmers. Cognitive Psychology, 13:307–325. Media Computation teachers (n.d.). Media Computation Teachers Website. Accessed October 2011. URL http://coweb.cc.gatech.edu/mediaComp-teach Meerbaum-Salant, O., Armoni, M., and Ben-Ari, M. (2010). Learning Computer Science Concepts with Scratch. In: Proceedings of the Sixth International Workshop on Computing Education Research, ICER ’10, pp. 69–76. ACM. Meerbaum-Salant, O., Armoni, M., and Ben-Ari, M. (2011). Habits of Programming in Scratch. In: Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education, ITiCSE ’11, pp. 168–172. ACM. Menand, L. (1997). Pragmatism: A Reader. Vintage. Meyer, J. H. F. and Land, R. (2003). Threshold Concepts and Troublesome Knowledge: Linkages to Ways of Thinking and Practising within the Disciplines. In: C. Rust (ed.), Improving Student Learning –TenYearsOn. Oxford Centre for Staff and Learning Development. Meyer, J. H. F. and Land, R. (eds.) (2006). Overcoming Barriers to Student Understanding: Threshold Concepts and Troublesome Knowledge. Routledge. Miller, G. A. (1956). The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information. Psychological Review, 63:81–97. Miller, L. A. (1981). Natural Language Programming: Styles, Strategies, and Contrasts. IBM Systems Journal, 20(2):184–215. Milne, I. and Rowe, G. (2002). Difficulties in Learning and Teaching Programming – Views of Students and Tutors. Education and Information Technologies, 7(1):55–66. Milne, I. and Rowe, G. (2004). OGRE: Three-Dimensional Program Visualization for Novice Programmers. Education and Information Technologies, 9(3):219–237. Minsky, M. (1974). A Framework for Representing Knowledge. AI Memo 306, Massachusetts Institute of Technology. MIT Media Lab (n.d.). Scratch (web site). Accessed October 2011. URL http://scratch.mit.edu/ Miura, M., Sugihara, T., and Kunifuji, S. (2009). Anchor Garden: An Interactive Workbench For Basic Data Concept Learning in Object Oriented Programming Languages. SIGCSE Bulletin, 41(3):141–145. 405
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Aalto University publication series
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Abstract Aalto University, P.O. Box
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Acknowledgements Lauri Malmi first
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4.5.3 Some effects of cognitive loa
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13.1.2 It is simple to watch an ani
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18.2.3 We saw a few pedagogically i
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11.16 PlanAni .....................
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Chapter 1 Here is How to Make Sense
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On research traditions Each researc
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The thesis should ideally be read f
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Introduction to Part I Introductory
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Figure 2.1: Bloom’s taxonomy of l
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2.2 The SOLO taxonomy sorts learnin
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2.2.3 The expected outcomes of prog
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Multi-institutional studies In the
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3.3 But many students do not learn
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long-term, action research study of
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Introduction to Part II What is inv
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Figure 4.1: A commonly used basic a
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play a decisive role in how and whe
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cognitive psychology also sought to
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terms for the two meanings. Followi
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It suggests that the growth of expe
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work (because of lack of motivation
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The effect of prior knowledge: the
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“writing a computer program is le
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model of program comprehension to o
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Chapter 5 Psychologists Also Say: W
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• are commonly deficient in a num
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expert stage. De Kleer and Brown ch
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5.3 Teachers employ conceptual mode
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Not only are there different notion
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the computer can carry out deductio
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Therefore: teach early and teach lo
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epresentation of a complex program
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A reasonable description of this fo
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are “somewhat contrary to the cla
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memory storage (see, e.g., Greeno,
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Chapter 6 Constructivists Say: Know
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Figure 6.1: A classification of con
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Social constructivist reasoning tak
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Figure 6.2: A radical destructivist
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certainly fall under the broad usag
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in the eyes of the members of the c
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of proper design, coding style, req
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In any particular course you will b
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precedes construction. Therefore, c
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Skirmish 7: minimal guidance pedago
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Chapter 7 Phenomenographers Say: Le
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the phenomenon. An individual’s e
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(Bowden and Marton, 2004). A way of
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The answer is none in particular, w
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Table 7.3: Different ways of experi
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7.6 Phenomenography has not escaped
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outcome spaces describe both the st
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Figure 8.1: Views from three tradit
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There is substantial agreement betw
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• it may mark boundaries in ‘co
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Telling TCs apart from other conten
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et al., 2007). The latter was later
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Part III Teaching Introductory Prog
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Chapter 10 CS1 is Taught in Many Wa
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it and right from the beginning. So
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10.1.3 Guidance mediates complexity
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10.2 Some approaches foster schema
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Many CS1 teachers have come up with
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Vagianou (2006) observes that a wea
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Figure 10.6: A view of Anchor Garde
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impressions of computing matter and
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The paradigm shift There is anecdot
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notional machines. Sajaniemi and Ku
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Program visualization vs. algorithm
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Figure 11.2: A part of Kelleher and
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aspects can play a decisive part in
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Table 11.1: The original engagement
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The 2DET The engagement taxonomy of
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Given content means that the learne
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Table 11.5: A summary of selected p
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Figure 11.4: The PyDev debugger for
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Figure 11.6: DynaLab executing a To
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Figure 11.10: The user has just com
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Figure 11.11: Korsh and Sangwan’s
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Figure 11.14: JIVE displays various
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Figure 11.16: PlanAni executing a P
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Figure 11.18: A snapshot of a Java
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Figure 11.19: Jeliot 3 executing a
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Figure 11.21: The Teaching Machine
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students used VIP in the way intend
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Python in the browser: Jype and the
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11.3.3 A few systems make the stude
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Figure 11.31: Students interact wit
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Online Tutoring System Kollmansberg
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Figure 11.36: A “Clouds & Boxes
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Table 11.7: Experimental evaluation
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Introduction to Part IV We have now
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An example Here is a short Python c
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Chapter 13 The UUhistle System Faci
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194 Figure 13.1: An animation of a
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Figure 13.2: The between-step stage
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Figure 13.4: UUhistle highlights a
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Figure 13.7: UUhistle’s interacti
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13.4 Teachers can turn examples int
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Add another +, then the literal 1.
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Figure 13.11: The user has created
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Figure 13.14: A VPS assignment in t
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Figure 13.16: A visual algorithm si
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Chapter 14 Visual Program Simulatio
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the individual instructions of the
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Visual program simulation seeks to
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14.2.2 VPS exercises can have eleme
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features prominently in many progra
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too, later decided to develop a sof
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The easiest examples for a programm
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may be necessary; at the very least
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Figure 14.1 continued M. H. van Emd
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integration of new material with pr
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The system designer vs. excessive d
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Level 3 By allowing what’s wrong.
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about this mistake to the user in a
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Table 15.1: Cognitive dimensions of
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parts that deal with different stag
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Error-proneness A VPS exercise in U
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something that resides ‘within th
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evidence. Like the documentation of
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Introduction to Part V Reflecting o
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launched for sharing (e.g., Fincher
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elatively independent of the resear
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There are values that are internal
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• Authenticity is an abstraction
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• Triangulation: triangulation of
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Each assignment was worth a number
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Figure 16.1: The earlier version of
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Chapter 17 Students Perceive Visual
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Figure 17.1: The phenomenographic r
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Figure 17.4: The structure of human
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Figure 17.5: The structure of aware
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further note the importance of esta
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Whichever variant of the analysis p
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Different phenomenographers define
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choose what to do?”, “What does
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We sought to form an outcome space
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17.4.1 A: VPS is perceived as learn
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17.4.2 B: VPS is perceived as learn
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Interviewer 2 : Can you describe ho
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17.4.5 E: VPS is perceived as learn
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through VPS is perceived as learnin
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Table 17.2: Qualitatively different
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what the phenomenon is in reality,
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the students develop a sophisticate
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involve VPS). 17 • The teacher us
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thought about the dynamics of VPS.
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solve the assignments using the ani
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Table 18.1: Types of information us
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As they start to work on the first
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print third second = third As they
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Elizabeth: And. . . So the function
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Otto: “True”. . . And then we g
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The pair fail to accommodate refere
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Parameter passing was the only topi
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18.3 We have some quantitative resu
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and the student searches for meanin
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in Section 18.2.4 is a step in this
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Chapter 19 UUhistle Helps Students
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• The supervising research assist
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Table 19.1: Improvement from ‘non
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- attempts to make variables get th
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to predict program behaviors better
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• The use of programs with a doma
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in the course more generally, not o
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Good both as an idea and in functio
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In many cases the UUhistle assignme
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Boredom and excessive detail A fair
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A handful of students suggested tha
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Part VI Conclusions 347
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Chapter 21 Visual Program Simulatio
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assignments but not focal to user a
Page 361 and 362: Chapter 22 What is Next for Visual
Page 363: 22.3 VPS can be taken to new users
Page 366 and 367: Appendix A Misconception Catalogue
Page 368 and 369: Table A.1 continued No. Topic Descr
Page 378 and 379: Assignment 1.7 (VPS) marks = float(
Page 380 and 381: else: print 'Better luck next time.
Page 382 and 383: ide.drive(15) print ride.get_fuel()
Page 384 and 385: def quaint(list, element): list[0]
Page 386 and 387: Appendix D 3×10 Bullet Points for
Page 388 and 389: Okay, what should I take into consi
Page 390 and 391: References ACM and IEEE Computer So
Page 392 and 393: Bareiss, R. and Radley, M. (2010).
Page 394 and 395: Biggs, J. B. and Collis, K. F. (198
Page 396 and 397: Burkhardt, J.-M., Détienne, F., an
Page 398 and 399: Cross, II, J. H., Hendrix, T. D., a
Page 400 and 401: Ehlert, A. and Schulte, C. (2009).
Page 402 and 403: Gilligan, D. (1998). An Exploration
Page 404 and 405: Harlow, S., Cummings, R., and Abera
Page 406 and 407: Johnson, R. and Onwuegbuzie, A. J.
Page 408 and 409: Ko, P. Y. and Marton, F. (2004). Va
Page 410 and 411: Lauer, T. (2006). Learner Interacti
Page 414 and 415: Miyadera, Y., Kurasawa, K., Nakamur
Page 416 and 417: Norman, D. A. (2007). Simplicity is
Page 418 and 419: Peterson, L. R. and Peterson, M. J.
Page 420 and 421: Renkl, A., Stark, R., Gruber, H., a
Page 422 and 423: Sajaniemi, J., Kuittinen, M., and T
Page 424 and 425: Shneider, E. and Gladkikh, O. (2006
Page 426 and 427: Stoodley, I., Christie, R., and Bru
Page 428 and 429: Vainio, V. (2006). Opiskelijoiden m
Page 430: Willingham, D. T. (2009). Why Don
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