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outcome spaces describe both the structure of learners’ understandings and the meanings learners assign to particular phenomena (see Section 7.1). This concrete information is helpful for teachers as an analytical tool and helps provide “a notional path of developmental foci for instruction” (Marton and Booth, 1997, p. 81). Taking the SOLO taxonomy to different contexts in a rigorous, empirically founded way is not an unworthy undertaking. In this sense, phenomenographic outcome spaces are considerably more than what you get by ‘simply’ applying SOLO to a phenomenon. 106
Chapter 8 Interlude: Can We All Just Get Along? The learning theories reviewed in the preceding chapters come from different research traditions, which use different kinds of research methods and base themselves on different ontological and epistemological assumptions. The final sections of Chapters 5, 6, and 7 show that the traditions and theories are in conflict with each other, with some heavy criticism flying in all directions. It makes sense to pause for a moment to consider whether it is sensible, or indeed possible, to make use of all these frameworks simultaneously. Section 8.1 below briefly compares the theories and traditions that I introduced in the previous chapters. In Section 8.2, I endorse combining theories to inform educational research from several perspectives. 8.1 There are commonalities and tensions between the learning theories Figures 8.1 through 8.4 list some claims about learning originating from cognitive psychology 1 (Chapters 4 and 5), constructivism (Chapter 6), and phenomenography (Chapter 7). The reader should note that each of the three headings within these tables corresponds to a broad area, and that these broad areas overlap with each other – there are many proponents of cognitive constructivism, for instance. My intention is not to make sweeping statements and claim, for example, that all constructivists believe every one of the claims I have attributed to constructivism. I merely seek to highlight a selection of significant claims that originate from, and have relatively wide support within, each tradition. The three traditions are in agreement on some points, in disagreement on others. Sometimes their claims are orthogonal with each other. Ontological and epistemological issues (Figure 8.1) are at the heart of many of the arguments between proponents of the various theories and research traditions; these concern the nature of learning as well as the trustworthiness of scientific knowledge and the research methods suitable for rigorous research. When it comes to how learning takes place, each tradition has its own points of emphasis (Figure 8.2). The cognitivist theories that I have reviewed tend to be the most detailed and specific in this respect. Phenomenographers tend to focus on the preconditions for learning which are realized in the variation present in the learning situation (as opposed to the generic processes that take place when the learner learns something). Many constructivist writers are very vague about knowledge representation and the processes of knowledge construction; some consider these to be idiosyncratic. Some constructivists – often with one foot in the cognitivist camp – make use of schema theory or conceptual change theory (Section 6.5). On the pedagogical side, there are clear tensions between the three camps, especially between strong forms of constructivism and others (Figure 8.3). Where constructivists tend to encourage authentic, complex learning activities that are carried out in collaboration with others and chosen by learners themselves to correspond to their own goals, various cognitive psychologists (Section 6.8) are careful to remind us of the importance of ‘managing’ learning so that cognitive load does not become excessive and is fruitfully employed through practice to generalize from instances to increasingly automated schemas. This does not always need to involve an authentic, complex context. 1 In Figures 8.1 through 8.4, “cognitive psychology” refers to schema theory, mental model theory, and cognitive load theory in particular. 107
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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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Page 66 and 67: 5.3 Teachers employ conceptual mode
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Page 82 and 83: Chapter 6 Constructivists Say: Know
Page 84 and 85: Figure 6.1: A classification of con
Page 86 and 87: Social constructivist reasoning tak
Page 88 and 89: Figure 6.2: A radical destructivist
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Page 98 and 99: precedes construction. Therefore, c
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Page 102 and 103: Chapter 7 Phenomenographers Say: Le
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Page 106 and 107: (Bowden and Marton, 2004). A way of
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Page 110 and 111: Table 7.3: Different ways of experi
Page 112 and 113: 7.6 Phenomenography has not escaped
Page 116 and 117: Figure 8.1: Views from three tradit
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Page 127 and 128: Part III Teaching Introductory Prog
Page 129 and 130: Chapter 10 CS1 is Taught in Many Wa
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Page 133 and 134: 10.1.3 Guidance mediates complexity
Page 135 and 136: 10.2 Some approaches foster schema
Page 137 and 138: Many CS1 teachers have come up with
Page 139 and 140: Vagianou (2006) observes that a wea
Page 141 and 142: Figure 10.6: A view of Anchor Garde
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Page 145 and 146: The paradigm shift There is anecdot
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Page 149 and 150: Program visualization vs. algorithm
Page 151 and 152: Figure 11.2: A part of Kelleher and
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Page 155 and 156: Table 11.1: The original engagement
Page 157 and 158: The 2DET The engagement taxonomy of
Page 159 and 160: Given content means that the learne
Page 161 and 162: Table 11.5: A summary of selected p
Page 163 and 164: 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
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Chapter 22 What is Next for Visual
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22.3 VPS can be taken to new users
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Appendix A Misconception Catalogue
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Table A.1 continued No. Topic Descr
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Assignment 1.7 (VPS) marks = float(
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else: print 'Better luck next time.
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ide.drive(15) print ride.get_fuel()
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def quaint(list, element): list[0]
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Appendix D 3×10 Bullet Points for
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Okay, what should I take into consi
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References ACM and IEEE Computer So
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Bareiss, R. and Radley, M. (2010).
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Biggs, J. B. and Collis, K. F. (198
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Burkhardt, J.-M., Détienne, F., an
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Cross, II, J. H., Hendrix, T. D., a
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Ehlert, A. and Schulte, C. (2009).
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Gilligan, D. (1998). An Exploration
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Harlow, S., Cummings, R., and Abera
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Johnson, R. and Onwuegbuzie, A. J.
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Ko, P. Y. and Marton, F. (2004). Va
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Lauer, T. (2006). Learner Interacti
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Markman, A. B. and Gentner, D. (200
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Miyadera, Y., Kurasawa, K., Nakamur
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Norman, D. A. (2007). Simplicity is
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Peterson, L. R. and Peterson, M. J.
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Renkl, A., Stark, R., Gruber, H., a
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Sajaniemi, J., Kuittinen, M., and T
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Shneider, E. and Gladkikh, O. (2006
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Stoodley, I., Christie, R., and Bru
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Vainio, V. (2006). Opiskelijoiden m
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Willingham, D. T. (2009). Why Don
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