Creative Learning in Digital and Virtual Environments

Opportunities and Challenges of Technology-Enabled Learning and Creativity

Vlad Glăveanu, Ingunn Ness, Constance de Saint Laurent, Vlad P. Glăveanu, Ingunn Johanne Ness, Constance de Saint Laurent

• Editore: Routledge
• Anno: 2020
• ISBN:
  9781000223323
• Edizione: 1
• Argomento: Éducation
• Categoria: Technologie de l'éducation

Originally published as a special issue of the Creativity Research Journal, this volume gives a balanced and reflective account of the challenges and opportunities of technology-enabled creative learning in contemporary societies.

Providing a current and updated account of the challenges posed by the Coronavirus to online education, chapters more broadly offer conceptual reflections and empirically informed insights into the impact of technology on individual and collective creativity and learning. These thoughts are explored in relation to school achievement, the development of digital educational resources, online collaboration, and virtual working. Further, the book also considers how the creative use of technology poses risks to learning through the accidental or deliberate dissemination of misinformation, and online manipulation of common societal values in the era of COVID-19.

Creative Learning in Digital and Virtual Environments looks at the connection between creativity, learning, and school achievement, and analyses the impact of virtual environments on creative expression. It will appeal to postgraduate students in the fields of creativity and learning, as well as to students and academics involved with broader research in areas such as the role of technology in education, e-Learning and distance education.

Vlad P. Gl?veanu is Associate Professor and Head of the Department of Psychology and Counselling at Webster University Geneva, Switzerland, as well as Associate Professor II at the University of Bergen, Norway.

Ingunn Johanne Ness is a Senior Researcher at the Centre for the Science of Learning & Technology, University of Bergen, Norway.

Constance de Saint Laurent is a Postdoctoral Researcher at the University of Bologna, Italy.

1
Delving Into Creativity and Learning

Maciej Karwowski, Dorota M. Jankowska, Arkadiusz Brzeski, Marta Czerwonka, Aleksandra Gajda, Izabela Lebuda, and Ronald A. Beghetto

Creativity researchers tend to view creativity and learning as related phenomena (Beghetto, 2016; Guilford, 1967), but there are different ways to conceptualise the nature of this relationship. A case can be made that creativity enhances learning through the use of imagination (Leopold, Mayer, & Dutke, 2018; Mayer & Gallini, 1990; Vygotsky, 2004), remote associations (Lee, Huggins, & Therriault, 2014), and originality (Dollinger, 2011). A case can also be made for learning serving as a necessary condition for creativity (Anderson & Krathwohl, 2001). Indeed, researchers have long recognised that prior knowledge and experiences serve as a basis for creative thinking (Guilford, 1950). It is also possible, however, to make a case that too great a focus on formal learning can hinder creativity (e.g., Simonton, 2017), and alternatively, placing too much emphasis on creative expression may hinder learning by making it less goal-oriented and unfocused (Baird, et al., 2012; Mooneyham & Schooler, 2013).

Empirical work examining this relationship has also provided somewhat variable findings. A recent meta-analysis (Gajda, Karwowski, & Beghetto, 2017) has offered a bit more clarity, indicating a positive, albeit modest correlation (r = .22) between creativity and academic achievement. Although there is both theoretical and empirical work suggesting a relationship between creativity and learning, many questions remain, including: What is the nature of the relationship between creativity and academic learning? Does this relationship differ across academic domains? Is academic learning a necessary condition for creative expression in a given academic domain? How might researchers more effectively measure both creativity and learning in an academic context?

In this chapter, we explore these questions by way of examining the links between creativity and learning. We open by briefly introducing a continuum that researchers may find helpful in conceptualising and assessing the relationship between creativity and learning. We then describe the Creativity and Learning in School Achievement Test (CLISAT), a new instrument developed with the goal of assessing creativity and learning in academic domains. Next we report on a study that had three goals: (1) examine the psychometric properties of the CLISAT (i.e., factor structure, reliability, and validity), (2) explore the relationship between CLISAT scores and measures of intelligence and divergent thinking, and (3) examine the conditional relationship between creativity and learning in math and language. We close with a brief discussion of implications for theory and research.

Conceptualising the Assessment of Creativity and Learning

There are an ever expanding array of methods and measures that researchers can use to examine potential links between creativity and academic learning (Freund & Holling, 2008; Gralewski & Karwowski, 2012). With respect to creativity, this includes approaches that range from self-report measures to the more objective creativity tests, including recent large-scale efforts aimed at measuring creative problem solving in large scale assessments (e.g., Programme for International Student Assessment 2021 measurement, but see Beghetto, in press).

Given these numerous options, there is little consensus in the field as to how to best measure creativity (see Kaufman, Plucker, & Baer, 2008). Similar ambiguity exists in the case of school achievement tests. Attempts to improve measurement of these constructs have been undertaken by several scholars (Alexander, Singer, Jablansky, & Hattan, 2016; Dumas & Alexander, 2016; Greiff, et al., 2013; McNeish & Dumas, 2017; Wüstenberg, Greiff, & Funke, 2012). With growing array of options available, how might researchers assess creativity and learning?

One way to address this question is to take a step back and recognise that all measures include assumptions about the nature of the constructs being assessed. Sometimes these assumptions are made explicit, other times they are not. Moreover, sometimes researchers inadvertently use measures that are based on assumptions about creativity that are not aligned with the goals of the study. As mentioned, creativity scholars typically conceptualise learning and creativity as related constructs. The assumptions about how to assess this relationship, however, can range on a continuum from viewing creativity and learning as separate constructs to mutually dependent or combined phenomena.

Figure 1.1 depicts this continuum of conceptualisations. Prior to discussing the various points on the continuum, it is important to note that the points are not meant to represent discrete categories, but rather highlight the variability in how creativity and learning can be assessed – ranging from approaches that attempt to assess the constructs separately to those that attempt to measure them simultaneously.

Figure 1.1 Continuum of conceptualising the assessment of creativity and learning

Note: CLISAT = Creativity and Learning in School Achievement Test

The continuum in Figure 1.1 spans conceptions that tend to use more general and separate measures of creativity (left side of the continuum) to those that use more situationally specific and blended approaches (right side of the continuum). The far left side of the continuum represents approaches that attempt to assess creativity and learning separately, using domain general assessments (e.g., domain general divergent thinking tests for creativity, grade point average for learning). Historically speaking, this is one of the most common ways that researchers have examined the relationship (see Gajda, Karwowski, & Beghetto, 2017).

In more recent years, however, scholars have emphasised the role that domain plays in both creativity and learning (Alexander, Schallert, & Reynolds, 2009; Baer, 2014). Consequently, there has been an effort to develop more domain or subject matter specific measures of both creativity and learning (e.g., Skiba, Tan, Sternberg, & Grigorenko, 2016). Given that academic domain seems to play an important role in both creativity and learning, approaches assessing creativity and learning using domain specific tasks form an important step toward improving the measurement of these constructs.

When scholars assess creativity and learning as separate constructs, they tend to focus on questions such as: What is the nature of the relationship between these two separate constructs? As they tailor those assessments to specific domains, they can also examine questions such as: What role does domain play in the relationship? and To what extent does prior learning serve as a necessary condition for creativity in specific domains (e.g., Anderson & Krathwohl, 2001; Krathwohl, 2002)?

Moving along the continuum, when scholars view creativity and learning as mutually dependent constructs, they tend to focus on how creativity and learning work more closely together in specific domains, contexts, and situations (Beghetto, 2016; Guilford, 1967; Vygotsky, 2004/1967). This perspective represents a long theoretical tradition in the field of creativity studies, which includes positing learning as a special case of a creative process (see also Guilford, 1950; Rubenstein, Callan, & Ridgley, 2018; Treffinger, Isaksen, & Firestien, 1983), creativity as a special case of learning (Rubenstein, et al., 2018), and even some combination of both, typically referred to as creative learning (see Beghetto, 2016). These perspectives resemble a classic Piagetian perspective (Piaget, 1973) that posits that students can memorise material but such memorisation is not sufficient to call it learning. Consequently, to really learn, personal understanding is required and this understanding is inherently associated with the transformation of the knowledge by the individual, so a creative act (Runco & Jaeger, 2007).

Creativity researchers who attempt to assess the mutually dependent relationship between creativity and learning focus on examining questions such as the following: How does prior experience convert into creative outcomes? How do creative insights lead to new understanding? What kinds of socio-situational factors influence creative learning of individuals? How do individual creative insights contribute to the learning of others?

There is limited but growing theoretical and empirical work along these lines. Such work focuses on clarifying the design logic needed for simultaneously assessing creativity and learning (Beghetto, in press) as well as demonstrating how more dynamic, blended, and situated methodologies can be used to conceptualise and study creative learning (Anderson, Haney, Pitts, Porter, & Bousselot, 2019; Gajda, Beghetto, & Karwowski, 2017; Jankowska, Czerwonka, Lebuda, & Karwowski, 2018; Karwowski, 2018).

The Creativity and Learning in School Achievement Test (CLISAT) falls somewhere between the separate and mutually dependent ends of the continuum (as illustrated in Figure 1.1). As we discuss in the next section, the CLISAT represents a step in the direction toward assessing creativity and learning in the same test, using domain specific tasks.

Introducing CLISAT

The CLISAT measures academic achievement and creative-learning relevant processes. As illustrated in Figure 1.2 (see Panel 1), it covers a broad range of subject-relevant content, while tasks capture different skills. Importantly, a number of tasks are devoted to measuring creativity-related operations, including the ability to associate, combine, modify, or transform ideas. Both parts of CLISAT – i.e., math and language – follow a similar structure (Panel 2). They are organised in separate, yet associated bl...

Indice dei contenuti

• Cover
• Half Title
• Series Page
• Title Page
• Copyright Page
• Contents
• Acknowledgements
• List of Contributors
• Introduction: Creativity, Learning, and Technology in the Age of the Pandemic
• 1 Delving Into Creativity and Learning
• 2 Social Creativity in the Education Sector: The Case of Collaborative Design of Digital Resources in Mathematics
• 3 Polyphonic Imagination: Understanding Idea Generation in Multidisciplinary Groups as a Multivoiced Stimulation of Fantasy
• 4 Networked Flow in Creative Collaboration: A Mixed Method Study
• 5 The Expression of Users’ Creative Potential in Virtual and Real Environments: An Exploratory Study
• 6 Malevolent Creativity and Social Media: Creating Anti-Immigration Communities on Twitter
• 7 Potential in Creativity: Individual, Social, Material Perspectives, and a Dynamic Integrative Framework
• 8 Creative Learning in Digital and Virtual Environments During COVID-19 and Beyond
• Index