Book Review: Achieving Deep Learning through Digital Technology
By Shalin Hai-Jew, Kansas State University
Figure 1. Strategies for Deep Learning with Digital Technology (Cover)
Strategies for Deep Learning with Digital Technology: Theories and Practices in Education
By Robert Z. Zheng, Editor
Nova Science Publishers
“Deep learning” is the holy grail of teaching and learning, particularly for the solving of complicated and real-world challenges. The editor defines deep learning as “learners’ engagement in critical and creative thinking, making inferences and transferring knowledge” (Zheng, 2018, p. xiii). While defined in different ways, “deep learning” involves complex understandings of the target subject domain (and related areas) that are transferable and that enable real-life problem solving and innovating in various contexts. In the Foreword, Douglas J. Hacker notes that the path to “deep, durable, and transferable learning” includes helping students deal with their preconceptions, organize knowledge in effective ways, and take control of their own learning, among others, in a 2004 report titled How People Learn: Brain, Mind, Experience, and School, by John D. Bransford, Ann L. Brown, and Rodney R. Cocking (The National Academy Press) (Hacker, 2018, pp. ix - x).
Given the contemporary harnessing of digital technologies in learning, are there ways to create and deploy these to enhance deep learning? Robert Z. Zheng’s Strategies for Deep Learning with Digital Technology: Theories and Practices in Education (2018) engages this question in rigorous research-based, theoretically informed, and data-centered ways.
Foundational Theory-based Understandings of Deep Learning
Michael K. Gardner lays a theoretical groundwork for deep learning from an educational psychology perspective in “The Psychology of Deep Learning” (Ch. 1) (but not fully updated to the present with neuroscience and other findings). Learning is broadly defined as “a relatively permanent change in knowledge or behavior caused by experience” (Woolfolk 2013, as cited in Gardner, 2018, p. 4). For people to process information in depth, processing has to occur at the semantic or meaning-based levels (not the orthographic or the phonological ones, not the level of language rules or language sounds) (Burgess & Weaver, 2003, as cited in Gardner, 2018, p. 7). Deeper knowledge processing enables better recall because “deep processing…results in more memory traces that are more distinctive” (Gardner, 2018, p. 7). This knowledge of human learning enables designing different types of mental rehearsal to strengthen memory and skills. It also enables minimizing interference with recall, such as incoming auditory signals during mental processing. Interestingly, because of “implicit memory,” it is possible to have deep learning even if it does not occur at a conscious level (Gardner, 2018, p. 15).
What are some other fundamentals? Knowledge may be separated into “declarative” (“episodic” and “semantic” memory) or “procedural” (performance of a task) (Sternberg & Sternberg, 2012, p. 219, as cited in Gardner, 2018, p. 15). Deep learning requires the ability to retrieve the knowledge from long-term memory based on cues (Gardner, 2018, p. 16) and to understand information in schemas (enhanced by “advanced organizers”) to connect new learning to older learning (Gardner, 2018, p. 18).
Practical Applications of Deep Learning
J. Michael Spector and Kaushal Kumar Bhagat’s “Promoting and Assessing Deep Learning Using Technology” (Ch. 2) take a macro societal view of deep learning. They identify recurring educational goals as complex problem solving skills [based on works by Dewey (1907, 1916); Spector (2015a), and Spector and Ren (2015)]. They express these skills as a stacked visualization, from foundation to top (with those at the top requiring more deep learning): “develop basic knowledge and skills – reading, writing and arithmetic; develop simple problem solving skills; develop a capable workforce; develop critical reasoning and higher order thinking skills along with creativity; develop responsible citizens and lifelong learners” (Spector & Bhagat, 2018, p. 39). The values driving this conceptualization are pro-social ones, with the idea that individuals help advance the social good, by solving multi-faceted and ill-structured problems without pre-written playbooks. This work will require thinking in systems thinking ways. While such skills require deep learning about a number of fields, some of which may require rote learning, the solutions themselves cannot be rote. Acquired knowledge may “be extended to new situations and circumstances, and that lasts a lifetime” (Spector & Bhagat, 2018, p. 39). The authors explore ways to promote “deep learning” in learners:
When applied to humans, deep learning involves such things as (a) understanding the complexities of dynamic problems, (b) determining the interactions among various components of a complex system, (c) identifying and influencing key variables influencing human learning, (d) designing effective and engaging learning environments, and (e) providing support for meaningful assessments of learning and evaluations of programs in support of learning (Spector & Bhagat, 2018, p. 37)
One program that the authors describe is based on DEEP and HIMATT technologies, which elicit information in a problem-solving activity, in order to create a node-link visualization of the key factors and interrelationships, resulting in an annotated “causal influence diagram” (Spector & Bhagat, 2018, p. 45), backed up by mathematical modeling. The systems enable collaborations from experts in a distributed way. The authors describe a case testing “the potential impact of a diesel tax on carbon dioxide pollution”—in order to consider complexity in various entity behaviors—and discovered the potential insight that “no significant long-term impact on reducing carbon dioxide pollution through diesel usage could be influenced” (Spector & Bhagat, 2018, p. 46). Other efforts are necessary.
Nada Dabbagh and Anastasia Kitsantas’ “Fostering Self-Regulated Learning with Digital Technologies” (Ch. 3) takes a social cognitive angle to supporting learners’ self-regulated learning through various technologies. Self-regulated learning (SRL) involves “goal-setting, self-monitoring, self-evaluation, time management, help seeking, motivation, and task strategies” (Dabbagh & Kitsantas, 2018, p. 56).
These authors use a somewhat idiosyncratic classification of technologies: learning management systems (LMSes), technologies (such as “web conferencing” tools), social networking technologies, mobile technologies, immersive technologies, cloud based technologies, (and) “experience & resource sharing technologies” (defined as “blogging, microblogging platforms, YouTube”) (Dabbagh & Kitsantas, 2018, pp. 53 - 54). Singly and in combination, these are personal learning environments (but these are also another category of technologies related to learning on their own). Self-regulated learning (SRL)
“...involves the regulation of cognition, behavior, and motivation. The cognitive area of SRL involves processes such as knowledge activation, goal-setting, metacognitive monitoring, and self-evaluation whereas motivation and affect refer to processes such as self-efficacy, task value beliefs, and interest. In terms of behavior, key SRL processes include behavioral monitoring where learners keep records of their progress, time allocation, effort, and environmental structuring” (Dabbagh & Kitsantas, 2018, p. 52).
In one of the studies, “social media technologies particularly experience and resource sharing tools such as blogs, wikis, microblogs, podcasts, and online bookmarking tools, engage learners in the SRL processes of goal setting, task strategies, self-monitoring and self-evaluation…(and) that social media were particularly useful in supporting learners’ motivation to learn through community engagement and inter-group communications facilitated through social networks” (Dabbagh & Kitsantas, 2018, p. 56). In their theorized model, various technologies may be harnessed to support learners in three main phases: forethought, performance, and self-reflection (Dabbagh & Kitsantas, 2018, p. 67).
Figure 2. Deep Learning (from Wikipedia, Wikimedia Commons), Color-Changed
Instructor Roles in Learner-mediated Collaborative Work Online
Byron Havard’s “Online Discussion Structure and Instructor Roles for the Promotion of Deep Learning” (Ch. 4) summarizes three main task types in learning through asynchronous discussion boards in LMSes: structured tasks (technical skills projects), structured topics (research papers), and collaborative tasks (comprehensive group projects), with related instructor roles of project lead, role model, and discussant respectively (Havard, 2018, p. 76). This work subscribes to the “high-tech high-touch” approach to online teaching and learning. To test this model, the author applied this approach to a graduate-level course, Web-Based Instruction, and collected learner responses.
A Meta-analysis of Simulation Research
Jennifer G. Cromley and LuEttaMae Lawrence’s “Multimedia Simulations that Foster Transfer Findings from a Review of the Literature” (Ch. 5) considers whether simulations “stimulate transfer more than regular teaching of the same subject” (Cromley & Lawrence, 2018, p. 93), and if there are ways to improve simulations for increased learning transferability. They define simulations:
A simulation is a specific multimedia learning tool that allows learners to manipulate variables to investigate and understand a specific phenomenon. Multimedia simulations facilitate a meaningful learning experience for students, by creating a real-world like experience within the limitations of a classroom or another restricted environment. Simulations use a wide variety of media types (i.e., graphs, schematic diagrams, narration, etc.), but are most frequently integrated with animation segments that are controlled by the learner, often using sliders. We define simulations to exclude haptics (touch interfaces), games with a learner objective, or animations without control over the presented content (Cromley & Lawrence, 2018, p. 94).
To select which research articles to include, they decided to evaluate published research in which the original researchers included “transfer” as one of the defined learning outcomes of the applied learning simulations. The co-researchers set standards for inclusion, with articles that “had to be (a) a simulation, including user control and not framed as a game, (b) published in a peer-reviewed journal since 2000, when modern computer interfaces became common, (c) published in English, (d) studying a math or science phenomenon, and € studying elementary through undergraduate participants. When they were part of an expert-novice study, adult participants were also included. In addition, (f) studies had to have dependent variable(s) that could be coded into one of the following categories: factual, inferential, procedural, or transfer. Each outcome had to fit into only one of the categories and (g) include enough information to allow us to derive an effect size. The learning outcome had to be an objective measure (rather than a self-report such as a self-report of perceived knowledge level) but could include tests, observations, practical exams, or other measures” (Cromley & Lawrence, 2018, p. 97). With those standards, the researchers identified 48 articles, with findings of 178 effects collectively, and 7,498 total participants in the respective studies. Only in 12 cases were transfer learning outcomes measured. A majority of the collected research focused on factual learning (73) and inferential learning (74). The authors identified 18 effects from 12 articles that “used a transfer learning outcome to understand how simulations foster deep learning experiences” (Cromley & Lawrence, 2018, p. 99).
Experience is identified an important differentiator between effective and ineffective problem solving, and the ability to engage in near-transfer vs. far-transfer:
Transfer is not simply determined by the completion of a transfer task. Individual differences that affect transfer, such as prior achievement, various aspects of motivation, amount of practice, and the nature of provided feedback, are also associated with transfer of learning (Nokes-Malach and Mestre 2013; Pellegrino and Hilton 2012). Transfer requires considerable practice to succeed; novice learners do not have the well-developed knowledge and skills to easily achieve transfer, especially when compared to experts (Pellegrino and Hilton 2012, as cited in Cromley & Lawrence, 2018, p. 95).
The experts come at challenges with “more developed and integrated prior knowledge and strategies” (Cromley & Lawrence, 2018, p. 95). For the researchers, instructional designers, and teachers using simulations, they may enhance the transfer effects of these with proper modifications and supports for learners at the proper times. One of the main conclusions: “When compared with learning in regular classroom settings, simulations can yield substantially better transfer (d = .57). The easy setup, instant feedback, and constrained problem space may provide these benefits.” (Cromley & Lawrence, 2018, pp. 107 - 108)
In general, simulations “yielded more transfer when they were built around less-familiar topics” (Cromley & Lawrence, 2018, pp. 106 - 107).
3D Virtual Modeling and Scientific Inquiry Skills
Kirsten R. Butcher, Michelle Hudson, and Madlyn Runburg’s “Visualizations for Deep Learning: Using 3D Models to Promote Scientific Observation and Reasoning during Collaborative STEM Inquiry” (Ch. 6) examines the learning impacts of 3D digitized authentic museum objects to increase scientific practices, such as close observation and documentation, inquiry, reasoning, and other work. To test their hypotheses, the co-authors set up Research Quest learning sequences, such as one asking: “What dinosaur did these bones come from?” and other STEM-inquiry-based scenarios (Butcher, Hudson, & Runburg, 2018, p. 115) Learners engage with 3D virtual models that they may explore; after this phase, learners describe what they experienced and make evidence-based argumentation in peer discussions.
On the research side, the co-researchers studied, in part, the amount of time spent rotating the photo-realistic 3D model to see if there was a relationship between that variable and positive and negative learning outcomes. Higher amounts of time spent rotating the digital artifact aligned with scientific-based “problem finding” and “generation of questions about the model” and “comparing,” but it also correlated with “evaluation without evidence” (for at least one of the simpler models) (Butcher, Hudson, & Runburg, 2018, p. 128). The visceral nature of the 3D realistic models may risk learners making “snap judgments without strong modeling on how the evidence gathered from digital models should be analyzed” (Butcher, Hudson, & Runburg, 2018, p. 129).
To mitigate such rushes to judgment, additional cognitive scaffolding may be built into the resource. This team included a built-in video that debriefs some of this with “the analysis of strong, weak, and disconfirming evidence drawn from the claw model after students had examined it” (Butcher, Hudson, & Runburg, 2018, p. 128). Overall, the amount of time spent rotating the object represented between “42(%) and 58% of the viewing time per model” (Butcher, Hudson, & Runburg, 2018, p. 128).
In terms of understanding what learners achieved in their learning experience, their comments were coded in ATLAS.ti using verbal protocol analysis. The authors included some evocative sample quotes along with the corresponding study imagery. The authoring team writes:
Generation of good observations was related strongly and positively to inferential processing. Thus, making tools available that encourage detailed inspection of objects may help students notice details and (by virtue of working in collaborative groups) allow them to verbalize details in ways that make them available for discussion and explanation (Butcher, Hudson, & Runburg, 2018, p. 130).
A predominant theory applied in instructional design practice today is Richard Mayer’s Cognitive Theory of Multimedia Learning (2014a, as cited in Taub, Mudrick, & Azevedo, 2018, pp. 141 – 142), which suggests that there are optimized ways of presenting information in digital media to enhance human learning by minimizing extraneous cognitive load. Michelle Taub, Nicholas V. Mudrick, and Roger Azevedo’s “Strategies for Designing Advanced Learning Technologies to Foster Self-Regulated Learning” (Ch. 7) focuses on “ALTs” and how well they enable self-regulated learning. They also base their work on a model by Greene and Azevedo (2009), with five macro-level processes (“planning, monitoring, strategy use, task difficulty and demands, and interest”) and related 35 micro-level processes (Taub, Mudrick, & Azevedo, 2018, p. 144). They define SRL as “students actively monitoring and regulating their cognitive, affective, metacognitive, and motivational processes” (CAMM) during learning instead of just passively consuming information (Taub, Mudrick, & Azevedo, 2018, p. 137). Some supporting tools include advanced organizers (like tables of contents), pedagogical agents, prompts, timer applications, multimedia design elements, user interface designs, and others (p. 138).
Figure 3. Techhnology (Pixabay)
Intelligent Browsing to Effective Instruction
Eric G. Poitras, Tenzin Doleck, Lingyun Huang, Shan Li, and Susanne P. Lajoie’s “nBrowser: An Intelligent Web Browser for Studying Self-Regulated Learning in Teachers’ Use of Technology” (Ch. 8) uses an information-structure approach in their design of a resource to enhance teachers’ usage of online resources to design learning. Their product is an “intelligent” web browser environment “designed to support student teachers to regulate their learning while navigating the web and designing a lesson plan that implements technologies into the classroom” (Poitrans, Doleck, Huang, Li, & Lajoie, 2018, p. 171). This approach is built in part on the TPACK (Technological Pedagogical Content Knowledge) framework, which centers the importance of teachers integrating technological, pedagogical, and content knowledge in their work. There is also the understanding of the importance of teaching teachers, who may use their sophistication and knowledge to benefit the learners that they work with.
They explain some of the technological functionalities of nBrowser:
Network-based tutors rely on web mining and natural language processing techniques to represent online resources as nodes interrelated by links weighed on the basis of different semantic relations. The properties of the network are continually updated by the tutoring system on the basis of learning behaviors, navigational trajectories, and the dimensions represented by the links and nodes featured in the network (Poitrans, Doleck, Huang, Li, & Lajoie, 2018, p. 173).
The authors provide evocative descriptions of how a teacher uses technologies to research the learning and to set it up for learners through lesson designs (Poitrans, Doleck, Huang, Li, & Lajoie, 2018, pp. 176 - 178). Their tool offers a way to set up lesson plan details, access available informational assets, and a lesson plan builder. The instructional principles informing their model include “the cohesion effect” (from informational nodes with “cohesive semantic relationships”), “the convergence effect” (resulting in “an optimal arrangement as activation spread through links at a rate proportional to the number of learners appraising the usefulness of online resources and nodes featured in the network”), “the emulation effect” as “allowing learners to emulate each other in their efforts to build a mental model of the pedagogical affordances of technology at a rate proportional to the number of learners that navigated through the network,” and the “disengagement effect” or “dysregulated learning behaviors” from “a network that fails to meet the conditions for cohesion, convergence, and emulation” (Poitras and Fazeli 2016a, as cited in Poitrans, Doleck, Huang, Li, & Lajoie, 2018, pp. 181 - 182). The system is designed to update dynamically to form “a cohesive collection of online resources that are useful to learning and task performance to inform system recommendations” (Poitrans, Doleck, Huang, Li, & Lajoie, 2018, p. 182). This “network-based tutor” is designed around particular subject domains (Poitrans, Doleck, Huang, Li, & Lajoie, 2018, p. 185). The system might be easier to understand if multiple walk-throughs were described to explain how users engage the system and come away with learning.
Huy P. Phan and Bing H. Ngu’s “An Empirical Examination of Goals, Student Approaches to Learning, an Adaptive Outcomes” (Ch. 9) proposes a conceptual model of learner learning progress. A first step in this model may either be towards approaching a learning topic of interest or avoiding it, with the first step supportive of learning, and the latter maladaptive. Then, the next step may be to a deep learning approach or a mal-adaptive surface learning one. Based on these early choices, results may be seen in the “academic absorption” (how engaged learners are with the topic), “academic dedication” (defined as “an individual’s sense of enthusiasm, pride, and inspiration for engaging in learning,” as cited in Schaufeli, et al., 2002, as cited in Phan & Ngu, 2018, p. 202), and “academic vigor” (defined as “an individual’s sense of persistence and resilience with the mobilization of effort,” as cited in Schaufeli, et al, 2002, as cited in Phan & Ngu, 2018, p. 202) and the respective implications of these three factors in overall learning achievement (Phan & Ngu, 2018, p. 199). The authors suggest that some learning paths are more efficacious than others and in statistically significant ways. And at heart, how students approach learning (through the “student approaches to learning” or SAL framework (Marton and Säljö 1976, Biggs 1987, as cited in Phan & Ngu, 2018, pp. 200 - 202) is an important issue. Based on research with live learners, the authors found the following: “Deep learning positively influenced the three components of engagement, whereas surface level negatively influenced dedication and vigor, but not absorption” (Phan & Ngu, 2018, p. 213).
Thinking around created learning artifacts. Silvia Hartung, Alexander Florian, and Bernhard Ertl’s “Supporting Students’ Reflective Practice Using the OneNote Class Notebook and Scaffolding” (Ch. 10) describes the harnessing of this Microsoft technology OneNote for building e-portfolios, to enable reflective practice and development of competencies.
Another work that focuses on teacher development is Fotini Paraskeva’s “Exploiting Innovative Technology-Enhanced Learning Environments for Teacher Professional Development” (Ch. 11). This work focuses on Technology-Enhanced Learning Environments (TELEs) for Teacher Professional Development (TPD), based on both the cognitive apprenticeship model and SRL. This involves learning through “collaboration, engagement, motivation and role playing” to enhance teacher expertise (Paraskeva, 2018, p. 249), beyond the traditional “training workshops, institutes, meetings, and in-service days” (Paraskeva, 2018, p. 250). The case described involves the Second Life® immersive virtual world and how “appropriate solid and interdisciplinary theoretical backgrounds” are needed to harness SL for effective learning purposes (Paraskeva, 2018, p. 255). The author proposes ways to use the tool affordances for “modeling, coaching, scaffolding, articulation, reflection and exploration (Collins et al. 1989, 2 – 10, as cited in Paraskeva, 2018, p. 257), particularly purposeful dyadic role playing, the building of e-portfolios, and self-reflection.
Scientific Inquiry and Social Competencies
In alignment with the interest in scientific inquiry, Muammer Ҫalik and Jazlin Ebenezer’s “Innovative Technologies-Embedded Scientific Inquiry Practices: A Socially Situated Cognition Theory” (Ch. 12) situates the importance of technologies in the teaching of science-based exploration and learning (especially scientific inquiry, investigations, and communication). The authors suggest that a social cognition approach not only enhances learners’ scientific skills but encourages them to engage “in issues that involve socio-cultural and political influences of science” (Ҫalik & Ebenezer, 2018, p. 269). To better integrate technology usage into the science curriculum, the co-authors introduces the Technology Embedded Scientific Inquiry (TESI) model…
that involves the use of technologies to develop a deeper understanding of subject-matter knowledge in order to test and clarify conceptual ideas. A handle of science knowledge is important to formulate research questions, shape investigations for solving problems, and provide evidence-explanation link for constructing arguments. Technology-embedded conceptualization studies have attempted to engage schools in science education reforms and science education standards (NRC 1996, 2000) by designing authentic learning activities, which intertwine content and process to target the development of students’ conceptual and epistemic understanding (i.e., Yaşar 2016; Yaşar et al., 2016, as cited in Ҫalik & Ebenezer, 2018, p. 281).
In this approach, the learning occurs in a social context to help learners conceptualize the science, investigate it, and communicate it.
The centrality of scientific practices in “scientific conceptualization” include “asking questions and defining problems…developing and using models…using mathematical and computational thinking…” “Scientific investigation” includes “planning and carrying out investigations,” “analyzing and interpreting data,” and “constructing explanations and designing solutions”. And “scientific communication” involves, in part, “engaging in arguments from evidence” and “obtaining, evaluating, and communicating information.” These standards guide “the science education of K-2, grades 3 – 5, grades 6 – 8, and grades 9 – 12” (NRC 2012, as cited in Ҫalik & Ebenezer, 2018, p. 277).
Chih-hsuan Wang, Brian W. Lebeck, and David M. Shannon’s “Fostering Deep Learning in an Online Learning Environment” (Ch. 13) builds a solid theorized case for the importance of learners acquiring and maintaining cognitive, intrapersonal, and interpersonal competencies, in order to achieve deep learning. This research team builds their case off of constructivism theory and the definition of deep learning as “learning that promotes the development of conditionalized knowledge and metacognition through communities of inquiry” (Weigel, 2002, p. 5, as cited in Wang, Lebeck, & Shannon, 2018, p. 306).
Learning is not a solitary achievement but something often achieved in community with others. Those communities need to be supportive of individual and group learning, respectful of individuals, supportive of rich intercommunications, and encouraging of reflection. Communities of inquiry are comprised of “three interdependent elements: 1) social presence, such as open communication and inter-personal relationship; 2) cognitive presence, such as triggering event, exploration, integration and resolution; and 3) teaching presence, such as course design, instructional facilitation, and directions” (Akyol and Garrison 2011, as cited in Wang, Lebeck, & Shannon, 2018, p. 306).
This authoring team offers recommendations for instructors working in online learning environments to foster interpersonal competencies with highly pragmatic approaches, such as setting up collaborative projects to encourage interaction between peers, holding virtual office ours and providing constructive feedback, designing course activities to support self-regulated learning strategies (such as having students keep learning journals), and creating learner self-assessment opportunities (Wang, Lebeck, & Shannon, 2018, pp. 325 - 326).
Interactive digital content to lighten cognitive loads? Kevin Greenberg and Robert Z. Zheng’s “A Study on Deep Learning and Mental Reasoning in Digital Technology in Relation to Cognitive Load” (Ch. 14) is based on an information processing conceptualization of deep learning, testing the theory that “having interactive content can decrease cognitive load” and so support deep learning. This theory is tested on learning contexts with high intrinsic cognitive load or instructional materials that are highly complex (Greenberg & Zheng, 2018, p. 338). Would presenting contents using interactivity lighten the extraneous cognitive load?
Their work is based around complex reasoning problems with “multiple rule-based problems” that require “complex, nonlinear thinking” that can generally only be solved through a series of cognitive activities including “analyzing, synthesizing, and evaluating while holding several conditions and rules in mind within a short time framework” (Greenberg & Zheng, 2018, p. 336). In a complex world, problem solving requires analysis to ensure that there are not unintended negative consequences or unintended blowback or undue costs. Making decisions with the challenges of built-in human cognitive biases, multiple players, incomplete information, and other challenges, requires avoiding superficial understandings and simplistic solutions.
The study to “investigate (a) the effects of digital technology on learners’ multiple rule-based problem reasoning and cognitive load, (b) the difference between level of rules in multiple rule-based problem reasoning and cognitive load, and (c) the effects of gender difference in multiple rule-based problem reasoning and cognitive load” (Greenberg & Zheng, 2018, p. 341) The authors describe their setup of the problem-based prompts with multiple restricting rules. They found that the interactive elements did not apparently alleviate working memory (Greenberg & Zheng, 2018, p. 351). It is wholly possible that some interactivity may be distractive instead of supportive of learning.
Human learning is complex, with ongoing research and discoveries about learners, educational technologies, and instructional methods. The idea is to use technologies purposefully and tactically, based on empirical data about their efficacy, not harness technology for its own sake. Robert Z. Zheng’s Strategies for Deep Learning with Digital Technology: Theories and Practices in Education (2018) is a solid and eminently readable book, with insightful contributions. This book is part of Nova Science Publisher’s series, Education in a Competitive and Globalizing World.
About the Author
Shalin Hai-Jew works as an instructional designer at Kansas State University. Her email is firstname.lastname@example.org.
Note: Thanks to Nova Science Publisher's for an electronic watermarked review copy of the book for review purposes.
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