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support for active learning, that is, the need to make the learner engage with what is to be learned. Compatible results have also been reported by researchers of educational visualization outside SV: videos and animations can be perceived as “easy” and their impact may be inferior to that of static images or even to not viewing a visualization at all, whereas interactive multimedia that cognitively engages the learner can lead to more elaborative processing (integration with prior knowledge) and consequently to better learning (see, e.g., Najjar, 1998; Scheiter et al., 2006, and references therein). In part, the benefit of active engagement may come simply from the fact that learners end up spending more time with the visualization, increasing the time spent on studying. Since the study of Hundhausen et al., SV researchers have increasingly sought to create teaching approaches and systems that promote learner involvement, and to further explore how different kinds of engagement impact on learning. My thesis can be seen as part of this movement. 11.2.2 Engagement taxonomies rank kinds of learner interaction Drawing on the work of Hundhausen and his colleagues, a sizable working group of SV researchers put their heads together to differentiate between the ways in which visualization tools engage learners (Naps et al., 2003). They presented an engagement taxonomy whose levels describe increasingly engaging ways of interacting with a visualization. The original engagement taxonomy (OET) Table 11.1 describes the six levels of the engagement taxonomy created by Naps et al. (2003), namely, no viewing, viewing, responding, changing, constructing, andpresenting. Naps et al. note that the levels after viewing do not form a strict hierarchy, but they do nevertheless hypothesize that using a visualization on a higher level of the taxonomy is likely to have a greater impact on learning than using it on one of the lower levels. (In Table 11.1, and elsewhere in this chapter, I use the term target software to refer to the programs or algorithms that are visualized by SV software, as opposed to the SV software itself. I will sometimes also refer to the target software as the content of a visualization.) SV systems sometimes feature different modes that engage students at different levels. Even a single activity may engage students at multiple levels of the taxonomy. Indeed, by definition, all activities at any but the no viewing level always involve viewing the visualization in addition to whatever else the student does. Naps et al. hypothesize that “more is better”: a mix of different engagement levels leads to better learning. The extended engagement taxonomy (EET) Myller et al. (2009) presented a more fine-grained version of the engagement taxonomy. Their extended engagement taxonomy, summarized in Table 11.2, introduces four additional levels and somewhat changes the definition of some of the original categories. The research focus of Myller et al. (2009) was on collaborative learning. They added to the hypotheses of Naps et al. by proposing that as the level of engagement between collaborating visualization users and the visualization increases, so does the communication and collaboration between the users. Empirical support The OET is grounded in the empirical work that inspired it, as reviewed by Hundhausen et al. (2002). Naps et al. (2003) discuss how these early experiments map onto the levels of the OET. The body of such work is not large enough to allow overall conclusions about the taxonomy to be drawn. Since the introduction of the OET and EET, some SV researchers (mostly on the AV side) have used them to set their research questions and to interpret prior work. Urquiza-Fuentes and Velázquez-Iturbide (2009) surveyed successful evaluations of software visualization systems in education, and relate these evaluations to the OET. They observed that various tool-supported visualization-based activities from the different levels of the OET have been found to be conducive to learning when compared to not using a visualization at all. A few evaluations also support the claims that students engaged in active forms of learning on the changing, responding, constructing, 146
Table 11.1: The original engagement taxonomy (OET) (based on Naps et al., 2003). # Level Description 1 No viewing The learner does not view a visualization at all. 2 Viewing The learner views a visualization. 3 Responding The learner responds to questions about the target software. 4 Changing The learner changes a visualization, e.g., by changing the input given to the target software. 5 Constructing The learner constructs a visualization, e.g., by drawing, by combining visual elements provided, by directly manipulating a predefined visualization, or by annotating source code to produce a visualization as the program is executed. 6 Presenting The learner explains a visualization to others. Table 11.2: The extended engagement taxonomy (EET) (based on Myller et al., 2009). # Level Description 1 No viewing The learner does not view a visualization at all. 2 Viewing The learner views a visualization without interacting with it by other means. 3 Controlled viewing The learner controls how he views a visualization, e.g., by changing the animation speed or choosing which objects to examine. 4 Entering input The learner enters input to the target software before or during execution. 5 Responding The learner responds to questions about the target software. 6 Changing The learner changes a visualization while viewing, e.g., via direct manipulation of the visualization’s components. 7 Modifying The learner modifies a visualization before viewing, e.g., by changing the target software or an input set. 8 Constructing The learner constructs a visualization interactively from components such as text and geometric shapes. 9 Presenting The learner presents a visualization to others. 10 Reviewing The learner views the visualization in order to provide feedback to others about the visualization or the target software. 147
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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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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 114 and 115: outcome spaces describe both the st
Page 116 and 117: Figure 8.1: Views from three tradit
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Page 122 and 123: Telling TCs apart from other conten
Page 124 and 125: et al., 2007). The latter was later
Page 127 and 128: Part III Teaching Introductory Prog
Page 129 and 130: Chapter 10 CS1 is Taught in Many Wa
Page 131 and 132: it and right from the beginning. So
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
Page 153: aspects can play a decisive part in
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
Page 165 and 166: Figure 11.6: DynaLab executing a To
Page 167 and 168: Figure 11.10: The user has just com
Page 169 and 170: Figure 11.11: Korsh and Sangwan’s
Page 171 and 172: Figure 11.14: JIVE displays various
Page 173 and 174: Figure 11.16: PlanAni executing a P
Page 175 and 176: Figure 11.18: A snapshot of a Java
Page 177 and 178: Figure 11.19: Jeliot 3 executing a
Page 179 and 180: Figure 11.21: The Teaching Machine
Page 181 and 182: students used VIP in the way intend
Page 183 and 184: Python in the browser: Jype and the
Page 185 and 186: 11.3.3 A few systems make the stude
Page 187 and 188: Figure 11.31: Students interact wit
Page 189 and 190: Online Tutoring System Kollmansberg
Page 191 and 192: Figure 11.36: A “Clouds & Boxes
Page 193: Table 11.7: Experimental evaluation
Page 196 and 197: Introduction to Part IV We have now
Page 198 and 199: An example Here is a short Python c
Page 200 and 201: Chapter 13 The UUhistle System Faci
Page 202 and 203: 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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