For several decades, extensive instructional research comparing the effects of different media on learning has been conducted, albeit with mixed results (Broadbent 1956; Clark 1983; Kinchla 1974; Kozma 1991; Mayer 2009; McLuhan 1964; Severin 1967). Researchers have debated whether educational technology (media) use is actually effective for improving student learning (Clark 1983; Kozma 1994; Tamin et al. 2011). Research in educational technology has moved past the classic debates that pervaded the educational literature between the 1980s and 90s. Rather than continuing the debates on media versus pedagogy, researchers have called for efforts to maximize the affordances of new technologies based on sound pedagogical principles (Kozma 1994). Hence, a plethora of studies have been published over the last two decades on multimedia learning and the use of learning technologies (Clark et al. 2016; Guri-Rosenblit and Gore 2011; Mayer 2009, 2014). However, development of new technologies continues to outpace research efforts on best practices for effectively using such technologies for learning. For example, the last few years have witnessed the emergence and extensive use of contemporary technologies such as the Flipped Classroom (FC), Massive Open Online Course (MOOC), Social Media, Serious Educational Games (SEG) and Mobile Learning (ML). While some of these new learning environments have been touted as panaceas, researchers and developers have been faced with enormous challenges in enhancing the use of these technologies to arouse student attention and improve persistent motivation, engagement, and learning (Annetta 2008; Hamlen 2011; Waks 2013; Yarbro et al. 2014). Broadly speaking, educational technologies have brought about developments and challenges in theory, methods, and practice. In the next section, we discuss theoretical, methodological, and practical developments and challenges with educational technologies. We caution that our review of these developments and challenges is not exhaustive, as such endeavor is beyond the scope of this chapter.
Theoretical Developments and Challenges with Educational Technologies
Because human learning , motivation, and engagement are highly complex, researchers have constructed several theories of human learning and instruction to explain these constructs (e.g., Jonassen and Land 2012; Mayer 2014; Reigeluth 2012). Among the more important recent theoretical advances relevant to emerging educational technologies are theories on multimedia learning, cognitive load, machine learning, data mining, learning analytics, and knowledge representation, and how they can be used to model human learning (Bottou 2014; Kirschner 2002; Markauskaite 2010; Martin and Sherin 2013; Mayer 2014; Plass et al. 2010). More recently, Michelle Chi and her team developed the ICAP framework (Chi and Wylie 2014). This framework provides theoretical underpinnings for the effects of educational technologies on different forms of cognitive engagement and the resulting learning outcomes. Other theoretical advances in the field of educational technologies are refinements or applications of long-standing psychological theories, including the social cognitive theory (Bandura 1989), its concomitant model of self-regulation (Zimmerman and Schunk 2001), and situated learning theory (Dawley and Dede 2013), especially legitimate peripheral participation and communities of practice (Lave and Wenger 1991) to explain student learning and engagement. For example, some contemporary educational technologies have incorporated online collaborative learning environments that facilitate learning with the help of others. Todayâs students increasingly use social, intelligent, and online learning environments to share ideas, get feedback, refine ideas, and publish information (e.g., Carter et al. 2017; Hundhausen et al. 2015; Kaufer et al. 2011; Ma et al. 2014; Maloney et al. 2010; Myneniet al. 2013). Hence, these long-standing psychological theories of learning have advanced the design of contemporary educational technologies and provided theoretical explanations for their benefits and challenges.
Although theoretical developments in educational technologies are advancing, new technologies are being developed at a rapid pace. This gives rise to the need for new theories to help researchers understand learning processes and outcomes. Some argue that existing theories of learning cannot sufficiently explain the fundamentally changed contextual conditions for learning brought about by advances in the technological landscape (Siemens 2005). More than ever before, new learning technologies help track and log learnersâ traces of their learning activity across different contextsâin school, at home, indoors, and outdoors (Martin and Sherin 2013). This generates rich, big data and a new wave of research questions (Greenhow et al. 2009; Reich et al. 2012).
Todayâs educational technologies provide fine-grained, process-oriented data at every click of the mouse. Tracking time spent online reading or working on a unit, notes taken, common errors, and other details can open up new pathways for understanding how people learn (Feng et al. 2009; Kramer and Benson 2013). SEG, intelligent tutors that provide formative feedback, MOOC courses or FCs, and posting reflections on electronic boards and blogs is part of daily life for many students. Such affordances of contemporary educational technologies require development of new learning theories and reconceptualization of research (DeBoer et al. 2014). The chapters in this book showcase affordances of contemporary and emerging educational technologies thus presenting a rich space for robust discussions on the role of existing theories and development of new theories to conceptualize and understand anticipated findings related to contemporary and emerging educational technologies.
Methodological Developments and Challenges with Educational Technologies
The field of educational technology has made great methodological strides. Methodological advances through the development of machine learning, data mining, and learning analytics have significantly expanded the research that can be carried out with contemporary educational technologies (Bottou 2014; Markauskaite 2010; Martin and Sherin 2013). More than ever, the use of technologies allows teachers, researchers, and instructional designers to track studentsâ interaction with learning resources and offer more real-time support for students. The nature of these technologies and the ability to ask rich research questions provide new opportunities to collect, analyze, and synthesize data in ways that were previously considered impractical. Based on this influx of data from rich research questions on both the process and outcomes of learning, educational researchers now harness statistical techniques such as hierarchical linear modeling, growth curve analysis, and latent profile analysis (Lee 2010) to advance our knowledge of human learning, engagement, and motivation.
Despite these methodological advances, several methodological challenges require immediate attention. For example DeBoer et al. (2014) argued for a reconceptualization by way of creating new educational variables or providing different interpretations of existing variables to more accurately understand the nature of MOOC data. They demonstrated the inadequacy of conventional interpretations of four variables for quantitative analysis (enrollment, participation, curriculum, and achievement). Although their research exclusively focused on MOOCs, similar issues may be fo...