e-Learning Ecologies

Principles for New Learning and Assessment

Bill Cope, Mary Kalantzis, Bill Cope, Mary Kalantzis

• Verlag: Routledge
• Jahr: 2017
• ISBN:
  9781317273356
• Auflage: 1
• Thema: Education
• Thema: Inclusive Education

e-Learning Ecologies explores transformations in the patterns of pedagogy that accompany e-learning—the use of computing devices that mediate or supplement the relationships between learners and teachers—to present and assess learnable content, to provide spaces where students do their work, and to mediate peer-to-peer interactions. Written by the members of the "new learning" research group, this textbook suggests that e-learning ecologies may play a key part in shifting the systems of modern education, even as technology itself is pedagogically neutral. The chapters in this book aim to create an analytical framework with which to differentiate those aspects of educational technology that reproduce old pedagogical relations from those that are genuinely innovative and generative of new kinds of learning. Featuring case studies from elementary schools, colleges, and universities on the practicalities of new learning environments, e-Learning Ecologies elucidates the role of new technologies of knowledge representation and communication in bringing about change to educational institutions.

1
Conceptualizing e-Learning

Bill Cope and Mary Kalantzis

On Learning Environments

This book explores phenomena we call e-learning ecologies. We use this metaphor because a learning environment is in some senses like an ecosystem, consisting of the complex interaction of human, textual, discursive, and spatial dynamics. These take a coherent, systemic form. Traditional classrooms, with their linear arrangement of seating and desks, their lecturing teachers, their textbooks, their student workbooks, and their classroom discussions are also learning systems. Moving from one of these classrooms to another, the modes of interaction are familiar and predictable because they are so systematically patterned. After a while, they seem “normal.” However, these are strange human artifacts that were not put together into this configuration until the nineteenth century. They quickly became universal and compulsory sites of socialization of mass-institutionalized education. In terms of the long arc of human history, it was not until about the time of our great-great-grandparents that we first encountered these modern educational systems. But will these institutional forms survive long into the twenty-fi rst century? Is it time for them to be reformed? And if change is to come, what will be the role of new technologies of knowledge representation and communication in bringing about change?

This book explores transformations in the patterns of pedagogy that accompany e-learning, or the use of computing devices to mediate or supplement the relationships between learners and teachers, to present and assess learnable content, to provide spaces where students do their work, and to mediate peer-to-peer interactions.

Our thesis is this: e-learning ecologies may play a key part in the largest shift in the systems of modern education since their rise to dominance in the nineteenth century. Everything may change—configurations of space, learner-to-teacher and learner-to-learner relationships, the textual forms of knowledge to which learners are exposed, the kinds of knowledge artifacts that students create, and the way the outcomes of their learning are measured. Or we may introduce a whole lot of technology into schools and nothing will change in institutional or epistemic senses. Technology is pedagogically neutral.

So our questions of e-learning ecologies become these: How can they be different? And why should they be different?

About This Book

This book is a collaborative work, written by the members of the “new learning” research group coordinated by Bill Cope and Mary Kalantzis, including colleagues, postdoctoral students, and graduate students at the University of Illinois. The group’s work has been in part conceptual; to create an analytical framework with which to differentiate those aspects of educational technology that reproduce old pedagogical relations from those that are genuinely innovative and generative of new kinds of learning. However, our work has been in equal measure practical. We have been working in schools, from elementary to college and university, experimenting with the practicalities of new learning ecologies. A focal point of this work for our team has been a research and development program that has resulted in the creation of the Scholar platform, supported by a series of research grants from the Institute of Educational Sciences in the US Department of Education and the Bill and Melinda Gates Foundation (see Figure 1.1). ¹ Research papers arising from this work are to be found here: http://newlearningonline.com/scholar/references.

Figure 1.1 The Scholar platform

The theoretical framework for this book—seven affordances for transformative e-learning ecologies—covers the same territory as the “e-Learning Ecologies” MOOC offered through Coursera. The companion website to this book, http://elearningecologies.com, presents the video mini-lectures that accompany this course. The book also reflects key themes developed for our learning design and leadership master’s and doctoral programs at the University of Illinois. At our http://elearningecologies.com website, you will also find a rich (and always growing) directory of e-learning case studies created by our graduate students.

Forces of Educational Change

e-Learning environments fall into two categories: new institutional sites of learning and traditional sites of learning that are being transformed by educational technologies. Striking new institutional forms include the rise of purely online learning and “virtual schools” (Molnar et al., 2014), the phenomenon of MOOCs (Knox, 2014; Waldrop, 2013), and “open education” (DeBoer et al., 2014; Peters & Britez, 2008). Traditional sites of learning are also undergoing transformation, including blended and ubiquitous learning (Cope & Kalantzis, 2009b, 2013), extending the range of classical classroom interactions beyond the physical classroom and class times and by one-to-one schools where every student has a device that they can take home.

In both new and traditional sites of learning, a range of new educational technologies is emerging. To a large degree, the same platforms are used in both new and traditional contexts. The following are some of the key educational technologies to emerge since the introduction of computer-mediated and online learning:

1. Learning management systems. Older systems include the commercial offering Blackboard and the open source offering Moodle. More recent commercial systems include D2L and Canvas. MOOC platforms, principally Coursera and edX, follow essentially the same pattern. Learning management systems align with the historical genre of the syllabus, laying out content to be covered and activities to be undertaken in a sequence, often ordered by time targets and deadlines. They may include readings, prerecorded videos, discussion areas, tasks, and assessments. A new feature of these systems is the possibility of learning analytics to track learner engagement, including not only traditional assessments and teacher gradebooks but also analyses based on incidental “data exhaust,” including keystroke patterns, edit histories, clickstream and navigation paths, and social interaction patterns (Cope & Kalantzis, 2016).
2. e-Textbooks. Replacing print textbooks, e-textbooks may include multimedia content and quizzes.
3. The flipped classroom. Low cost, easily accessible video recording and web upload of teacher lectures (Bishop & Verleger, 2013).
4. Intelligent tutors, games, and simulations. These guide a learner through a body of knowledge, serving content, requesting responses, making hints, offering feedback on these responses, and designing stepwise progression through a domain depending on the nature of these responses (Aleven, Beal, & Graesser, 2013; Chaudhri et al., 2013; VanLehn, 2006). Underlying intelligent tutors, games and simulations are cognitive models that lay out the elements of a target domain, anticipating a range of learning paths (Conrad, Clarke-Midura, & Klopfer, 2014). Intelligent tutors work best in problem domains where highly structured progressions are possible, such as algebra or chemistry (Koedinger et al., 2013). They are less applicable in areas where progression cannot readily be assembled into a linear sequence of knowledge components (Graesser et al., 2001).
5. Discussion boards. These substitute for the oral discussions of the traditional classroom, supporting various forms of conversational interaction. Patterns of peer interaction can be mapped—who is participating, with whom, and to what extent (Speck et al., 2014; Wise, Zhao, & Hausknecht, 2013). Natural language processing methods can be used to parse the content of interactions (Xu et al., 2013).
6. Web workspaces and e-portfolios. Contemporary student workspaces differ from traditional pen-and-paper student activity in several key respects, including expansion of the media of knowledge representation, the ease of collaborative work, and the possibility of sharing completed work in e-portfolios. These spaces also support logistically complex, highly structured interactions such as peer review. Using a single cloud-located source, it is possible to manage what is otherwise a difficult-to-administer process of anonymization, randomization, and simultaneous review by multiple reviewers (Abrams, 2013; Cope & Kalantzis, 2013; Kline, Letofsky, & Woodard, 2013; Lammers, Magnifico, & Curwood, 2014; McCarthey et al., 2014).
7. Adaptive, personalized, and differentiated instruction. Such systems monitor differential learning progress from student to student and adapt the path and pace of learning to the speed at which the learner is progressing. This represents a break from the logics of one-size-fi ts-all, everyone-on-the-same-page of traditional classrooms, continuously calibrating learning to individual needs (Conati & Kardan, 2013; Koedinger et al., 2013; Shute & Zapata-Rivera, 2012; Walkington, 2013; Wolf, 2010; Woolf, 2010).
8. Machine assessments. Two principal kinds of machine assessment have emerged with the use of computing in education: computer adaptive testing and natural language processing (Cope et al., 2011; Vojak et al., 2011). Computer adaptive testing extends long-standing item response theory, where correct student response to test items varies according to what the student knows or understands (a latent cognitive trait) and the relative difficulty of the item. Computer adaptive tests serve students progressively harder or easier questions depending on whether they answer correctly or incorrectly. Such tests provide more accurately calibrated scores for students across a broader range of capacities, reach an accurate score faster, and are harder to game because no two students end up taking quite the same test (Chang, 2015). One variant of these assessments, computer diagnostic testing, allows for the coding of topic areas within a test and disaggregation of scores within the subdomains addressed by the test (Chang, 2012). In another major form of machine assessment, natural language processing technologies are today able to grade short-answer and essay-length supply-response assessments with reliability equivalent to human graders (Burstein & Chodorow, 2003; Chung & Baker, 2003; Cotos & Pendar, 2008; Shermis, 2014; Warschauer & Grimes, 2008). Natural language processing offers two types of tools for writing assessment, often used in concert with each other: statistical corpus comparison and analytical text parsing (Cope, Kalantzis, & Magee, 2011). In the case of the corpus comparison, the computer is “trained” by being given a corpus of human-graded texts; the machine compares new texts and grades them based on statistical similarity with the human-graded texts. In the case of text parsing, computers are programmed to search for language features, such as markers of textual cohesion, the range and complexity of vocabulary, and latent semantics based on w...