Learning Mechanisms Explaining Learning with Digital Tools in Educational Settings: A Cognitive Process Framework
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| Title: | Learning Mechanisms Explaining Learning with Digital Tools in Educational Settings: A Cognitive Process Framework |
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| Language: | English |
| Authors: | Frank Reinhold (ORCID |
| Source: | Educational Psychology Review. 2024 36(1). |
| Availability: | Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ |
| Peer Reviewed: | Y |
| Page Count: | 21 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Descriptive |
| Descriptors: | Cognitive Processes, Intellectual Disciplines, Learning Activities, Technology Uses in Education, Instructional Design, Cognitive Psychology, Teaching Methods, Educational Psychology |
| DOI: | 10.1007/s10648-024-09845-6 |
| ISSN: | 1040-726X 1573-336X |
| Abstract: | To explain successful subject matter learning with digital tools, the specification of mediating cognitive processes is crucial for any empirical investigation. We introduce a cognitive process framework for the mechanisms of learning with digital tools (CoDiL) that combines core ideas from the psychology of instruction (utilization-of-learning-opportunity framework), cognitive psychology (knowledge-learning-instruction framework), and domain-specific research on learning and instruction. This synthesizing framework can be used to theoretically ground, firstly, the design of digital tools for learning, and secondly, the empirical analysis of students' learning activities in digitally enriched educational settings via the analysis of specific student-tool interactions. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1409433 |
| Database: | ERIC |
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| FullText | Links: – Type: pdflink Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwG61F1JK8GVMrnsD2Pmis1GAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDEKgG_c7rtlrpvPAZAIBEICBm5IQV_zMW28IECtl5eiBTWP0A0sJneSkPT8BKo4NIPFCEmj-cmd3aWQpf5WAGE7FneYCDiuLgFhU7BnZkFmUypxxZHOh_4eAFrNbq42H9RCn4UNHyoOtNfFzXCsmg_h5hLsYqy37i_k0F0tz0OtJTEm5BSaeZbd44LWah3Hu-NPNHZVy3gk04FYOzjKJC7IVE_g7mycQDYwY46TR Text: Availability: 1 Value: <anid>AN0175123852;epv01mar.24;2024Apr20.13:03;v2.2.500</anid> <title id="AN0175123852-1">Learning Mechanisms Explaining Learning With Digital Tools in Educational Settings: a Cognitive Process Framework </title> <p>To explain successful subject matter learning with digital tools, the specification of mediating cognitive processes is crucial for any empirical investigation. We introduce a cognitive process framework for the mechanisms of learning with digital tools (CoDiL) that combines core ideas from the psychology of instruction (utilization-of-learning-opportunity framework), cognitive psychology (knowledge-learning-instruction framework), and domain-specific research on learning and instruction. This synthesizing framework can be used to theoretically ground, firstly, the design of digital tools for learning, and secondly, the empirical analysis of students' learning activities in digitally enriched educational settings via the analysis of specific student-tool interactions.</p> <p>Frank Reinhold, Timo Leuders, and Katharina Loibl contributed equally to this work.</p> <hd id="AN0175123852-2">Introduction</hd> <p>Digital technologies have not yet fully realized their potential in education, although the findings of meta-analyses show that their implementation into the classroom can have a positive impact on student achievement (e.g., Chauhan, [<reflink idref="bib12" id="ref1">12</reflink>]; Higgins et al., [<reflink idref="bib35" id="ref2">35</reflink>]; Hillmayr et al., [<reflink idref="bib36" id="ref3">36</reflink>]; Q. Li &amp; Ma, [<reflink idref="bib57" id="ref4">57</reflink>]; Moran et al., [<reflink idref="bib69" id="ref5">69</reflink>]; Tamim et al., [<reflink idref="bib101" id="ref6">101</reflink>]). It is commonly agreed upon that this impact on student achievement is <emph>not</emph> due to digital technology itself, but rather due to specific digitally enriched instructional features (e.g., adaptive feedback, prompting, scaffolding) implemented into those technologies and the specific use teachers and students make of these features (see the media debate, started by Clark, [<reflink idref="bib16" id="ref7">16</reflink>]; Kozma, [<reflink idref="bib49" id="ref8">49</reflink>]). Still, research often focuses on the effects of the mere presence of digital tools on learning outcomes (Kucirkova, [<reflink idref="bib50" id="ref9">50</reflink>]).</p> <p>When inspecting the cause of such positive impact of digital tools in educational contexts, however, there may not be one single satisfactory answer (Hillmayr et al., [<reflink idref="bib36" id="ref10">36</reflink>]): not only does the term 'digital tool' encompass a broad variety of different types of software and hardware that can be used in very different ways, research also has demonstrated that different types of digital tools can have specific benefits (Chi &amp; Wylie, [<reflink idref="bib14" id="ref11">14</reflink>]; Hillmayr et al., [<reflink idref="bib36" id="ref12">36</reflink>]; Koedinger et al., [<reflink idref="bib47" id="ref13">47</reflink>]; VanLehn, [<reflink idref="bib103" id="ref14">103</reflink>]). That is one reason why measuring learning outcomes of <emph>digitally enriched instruction</emph> (i.e., instruction that is enriched by digital technology) and comparing these to other types of instruction does not suffice to theoretically underpin the learning mechanisms at play when students utilize technology during learning.</p> <p>Therefore, research on <emph>why</emph> learning with digital tools works should refer to theories on cognition <emph>during</emph> learning, should be specific about the assumed underlying <emph>learning activities</emph> which the <emph>digitally enriched instructional features</emph> implemented aim to enhance, and should account for the characteristics of the <emph>content</emph>. When analyzing such features of digitally enriched learning environments, it is worthwhile to consider that their positive effect on student learning may be rooted in very different psychological constructs. For example, while metacognitive scaffolds inside an online math tutor may help students regulate their learning, simulations inside a digital physics learning environment may guide students into reasoning about the physical phenomenon. Consequently, we suggest to focus not only on learning <emph>outcomes</emph>, but also on the specific effects of features implemented in digital tools on students' learning activities and their variation with different learning contexts and content. To draw attention to this connection we use the term "(digitally enriched) instructional features" for a broad variety of different components and functionalities of software (such as, scaffolds, adaptive feedback, simulations of phenomena, prompts), designed and implemented to stimulate and aid learning activities.</p> <p>In this paper, we present a framework which has the purpose of guiding theoretical analyses and empirical research that aim to explain learning in digitally enriched educational settings via instructional features and learning activities. For this, we draw on three frameworks or models which generally capture the structure and dynamics of learning in such settings. First, we refer to the <emph>knowledge-learning-instruction framework</emph> by Koedinger and colleagues (KLI; Koedinger et al., [<reflink idref="bib47" id="ref15">47</reflink>]) and elaborate how this framework is well suited to describe learning with digital tools by distinguishing between external instructional events and assessment events on the one hand, and internal learning processes and knowledge components on the other hand. Second, the SOI-Model (<emph>Selection-Organization-Integration</emph>) defines the internal cognitive core processes during learning (Mayer, [<reflink idref="bib65" id="ref16">65</reflink>]). According to the model, the learner actively constructs knowledge representations in the working memory using both incoming material from the environment and prior knowledge in the long-term memory. This active construction includes <emph>selection</emph>: identification of useful information, <emph>organization</emph>: understanding how the single information interact with each other, and <emph>integration</emph>: relating new information with prior knowledge. We further argue that an appropriate approach to capture learning should account for the systematic distinction between digitally enriched learning <emph>opportunities</emph> and students' actual learning <emph>activities</emph> during their use—since these may vary substantially among students. Such an approach is described by so-called <emph>utilization-of-learning-opportunity frameworks</emph> from classroom research (ULO; Seidel, [<reflink idref="bib95" id="ref17">95</reflink>]; see also Brühwiler &amp; Blatchford, [<reflink idref="bib11" id="ref18">11</reflink>]). ULO frameworks distinguish explicitly between potentially used learning opportunities on the one hand (e.g., educational settings that have the potential for cognitive activation—a core dimension of instructional quality representing offers of challenging tasks and stimulating ways of interacting with the subject matter and with the peers; see Praetorius et al., [<reflink idref="bib77" id="ref19">77</reflink>]) and actual student engagement on the other hand (i.e., the mental effort learners invest in genuine knowledge construction processes—germane processing; Sweller et al., [<reflink idref="bib100" id="ref20">100</reflink>]; see Nückles, [<reflink idref="bib72" id="ref21">72</reflink>], for a discussion).</p> <p>Since the perspectives of these frameworks appear highly relevant for empirically investigating and theoretically explaining learning in digitally enriched educational settings, we introduce the <emph>CoDiL framework</emph>—a <emph>Cognitive process framework on the Learning mechanisms of Digital tools in educational settings</emph> that incorporates both the core ideas of the KLI framework, and the ULO framework—i.e., core ideas from empirical classroom research, cognitive psychology, and subject-specific as well as content-specific education research (Fig. 1). For an analogous framework for composite instructional designs (i.e., instructional designs with multiple phases) see Loibl et al. ([<reflink idref="bib61" id="ref22">61</reflink>]).</p> <p>Graph: Fig. 1The CoDiL framework, a cognitive process framework on the learning mechanisms of digital tools in educational settings. The dashed separates the external situation and external behavior on the left, and the internal person characteristics and cognitive processes on the right. The structure of the utilization-of-learning opportunity framework (ULO) is represented by the dashed boxes. The structure of the knowledge-learning-instruction framework (KLI) is indicated by the three columns</p> <p>In the context of the CoDiL framework, <emph>instruction</emph> is understood as a digitally enriched educational setting (see the "Acquiring Knowledge Components in (Digitally Enriched) Educational Settings" section), in which the used digital tools can be characterized by their implemented digitally enriched instructional features (see the "Digital Instruction—Instructional Features as Opportunities Stimulating Learning Activities" section). <emph>If</emph> utilized by the student these features (which offer learning opportunities) are considered to initiate and guide learning activities (see the "Learning Activities When Learning With Digital Tools" section). These learning activities have a behavioral side (i.e., external, and directly observable, e.g., interacting with the digital tool, solution formulation, or written self-explanations; see the "Evaluation—Linking Students' On-Task Behavior and Learning" section) and a cognitive side (internal and latent, e.g., adaption, discrimination, or revision; see the "Learning Activities When Learning With Digital Tools" section). In interaction with prior knowledge—and mediated by cognitive processes (i.e., selecting, organizing, integrating)—the learning activities cause evolving knowledge during learning—leading to final knowledge at the end of the learning process.</p> <p>By representing a high-level conceptual integration of different theories, the CoDiL framework serves to systematically describe (see the "Learning as Individual Content-Specific Cognitive Processes to Generate Knowledge" section), develop (see the "Development—Designing Digitally Enriched Educational Settings" section), and evaluate (see the "Evaluation—Linking Students' On-Task Behavior and Learning" section) research on digitally enriched instruction: we show how it can be utilized to theoretically ground the design of digital tools for educational settings, why it is suitable to frame correlational or causal research endeavors, and how it can guide the evaluation of students' learning activities in digitally enriched educational settings based on appropriate operationalizations of student-tool interactions. We exemplify the applicability of our framework by framing recent studies regarding the effect of learning with digital tools with regard to our framework.</p> <hd id="AN0175123852-3">Learning as Individual Content-Specific Cognitive Processes to Generate Knowledge</hd> <p></p> <hd id="AN0175123852-4">Acquiring Knowledge Components in (Digitally Enriched) Educational Settings</hd> <p>In line with established learning theories, we understand learning as an active, constructive, and content-specific cognitive process (Koedinger et al., [<reflink idref="bib47" id="ref23">47</reflink>]; Mayer, [<reflink idref="bib65" id="ref24">65</reflink>]). Consequently, a framework which aims at explaining learning should explicate on the one hand external, observable, and manipulable elements—e.g., <emph>instructional events</emph> and observable <emph>student-tool interactions—</emph>and on the other hand model internal and unobservable learning processes and learning outcomes—e.g., <emph>knowledge components</emph> (Koedinger et al., [<reflink idref="bib47" id="ref25">47</reflink>]; Yeo &amp; Fazio, [<reflink idref="bib108" id="ref26">108</reflink>]).</p> <p> <emph>Knowledge components</emph> are connected in multiple ways to other elements in the CoDiL framework. Similar to the construct of <emph>schemata</emph> (Schweppe &amp; Rummer, [<reflink idref="bib93" id="ref27">93</reflink>]), Koedinger and colleagues ([<reflink idref="bib47" id="ref28">47</reflink>]) "define a knowledge component ... as an acquired unit of cognitive function or structure that can be inferred from performance on a set of related tasks" (p. 764). The knowledge components define the structure of the final knowledge, but they also specify the prior knowledge that can be activated during learning and the evolving knowledge (including incomplete or erroneous knowledge components) that develops throughout the learning process. Since the knowledge components available at any point in time influence the learning activities, a content-specific analysis of these knowledge components is a fundamental basis for understanding the learning mechanisms at play, and thus a fundamental basis for the design and development of any educational environment that aims to achieve this knowledge acquisition. In particular, we emphasize the argument that a subject-specific analysis of the content (as part of, e.g., adaptive control of thought-rational, Anderson et al., [<reflink idref="bib4" id="ref29">4</reflink>]; construction-integration model, Kintsch, [<reflink idref="bib45" id="ref30">45</reflink>]; cognitive modeling, Ritter et al., [<reflink idref="bib85" id="ref31">85</reflink>]) is relevant for the development of digitally enriched educational settings and the design of digital tools.</p> <p>Consider, for example, students learning how to compare the size of fractions, such as 2/3 and 4/5. Here, decades of theoretical and empirical research draw a very clear picture of the prior knowledge (natural number concepts, the part-whole concept, and the concept of fraction equivalence; Post &amp; Cramer, [<reflink idref="bib76" id="ref32">76</reflink>]) influencing the students' proficiency, various faulty, or still evolving knowledge aspects (isolated comparisons of numerator or denominator as natural numbers in different forms; Gómez &amp; Dartnell, [<reflink idref="bib26" id="ref33">26</reflink>]), and the final knowledge (repertoire of various correct comparison strategies; Clarke &amp; Roche, [<reflink idref="bib17" id="ref34">17</reflink>]). These different knowledge components can be used to design an explanatory cognitive model of how students learn to compare fractions: given the influence of different kinds of prior knowledge, the pathway to a desirable change in knowledge may be very different for students who overgeneralize natural number concepts, or students who already have acquired the part-whole concept. Given such cognitive student models, content-specific educational settings (as opportunities for learning) can be designed to initiate and stimulate specific learning activities ("Learning Activities When Learning With Digital Tools" section) that may lead to the final knowledge.</p> <p>In general, content-specific prior knowledge should be considered relevant when discussing educational settings (Kalyuga, [<reflink idref="bib42" id="ref35">42</reflink>]; Simonsmeier et al., [<reflink idref="bib97" id="ref36">97</reflink>]), since every learner has some domain-general prior experiences, domain-specific knowledge facets, or an attitude or interest toward the learning domain (Tobias, [<reflink idref="bib102" id="ref37">102</reflink>]). Prior knowledge is the key predictor of how information is selected, organized, and consequently, how evolving knowledge arises (Kalyuga, [<reflink idref="bib43" id="ref38">43</reflink>] and Simonsmeier et al., [<reflink idref="bib97" id="ref39">97</reflink>] for a meta-analysis). This evolving knowledge is further elaborated and consistently integrated into already existing knowledge structures (Mayer, [<reflink idref="bib65" id="ref40">65</reflink>]). Consequently, all knowledge components have to be carefully considered when discussing the learning process from the initial start of the learning phase and the individual perquisites of the learner to the processing of new information that gets integrated into existing knowledge and constantly updated (i.e., <emph>evolving knowledge</emph>) until the final knowledge is reached. Here, cognitive (content-specific) student models may serve as guidelines.</p> <hd id="AN0175123852-5">Learning Activities When Learning With Digital Tools</hd> <p>Yet, building the design of digitally enriched learning environments on such rich cognitive models of student knowledge does not guarantee learning success for <emph>each</emph> student. Therefore, the utilization-of-learning-opportunity framework (ULO) seeks to integrate structural aspects of learning environments and actual processes that students undergo <emph>during</emph> instruction (Seidel, [<reflink idref="bib95" id="ref41">95</reflink>]; see also Brühwiler &amp; Blatchford, [<reflink idref="bib11" id="ref42">11</reflink>]). The ULO framework is commonly used as a domain-general framework in educational effectiveness research and focuses on teacher-student interaction on a classroom level, describing why students differ in how they succeed in learning scenarios (Alp Christ et al., [<reflink idref="bib3" id="ref43">3</reflink>]; Seidel &amp; Shavelson, [<reflink idref="bib96" id="ref44">96</reflink>]). This domain-general description works very well for domain-general measures of how students utilize learning opportunities, such as classroom engagement (Fredricks et al., [<reflink idref="bib23" id="ref45">23</reflink>]; Guertin et al., [<reflink idref="bib31" id="ref46">31</reflink>]; Henrie et al., [<reflink idref="bib34" id="ref47">34</reflink>]; Huang et al., [<reflink idref="bib39" id="ref48">39</reflink>]; Lo &amp; Hyland, [<reflink idref="bib59" id="ref49">59</reflink>]). In its usual application, the main argument of the ULO framework is that for learning to be effective (in terms of students demonstrating high learning outcomes), students have to <emph>actively engage</emph> in the learning opportunities offered. This underpins that learning is considered to be an active and generative process—in line with generative learning theory (Fiorella &amp; Mayer, [<reflink idref="bib22" id="ref50">22</reflink>]; Roelle &amp; Nückles, [<reflink idref="bib88" id="ref51">88</reflink>]) and theories about self-regulation (Azevedo, [<reflink idref="bib6" id="ref52">6</reflink>]; S. Li et al., [<reflink idref="bib58" id="ref53">58</reflink>]; Molenaar et al., [<reflink idref="bib68" id="ref54">68</reflink>]).</p> <p>However, for disentangling each student's utilization of learning opportunities provided by digital tools, the conceptualization of learning mechanisms needs to focus on individual learning activities and to be content-specific (e.g., Reinhold et al., [<reflink idref="bib80" id="ref55">80</reflink>]). For that, the CoDiL framework describes learning opportunities in digitally enriched educational settings, which are characterized via specifically designed digitally enriched instructional features ("Digital Instruction—Instructional Features as Opportunities Stimulating Learning Activities" section). These instructional features may initiate and stimulate <emph>learning activities</emph>. We understand such learning activities in digitally enriched educational settings as (<reflink idref="bib1" id="ref56">1</reflink>) activities that students do (or do not) engage in when working within the digitally enriched educational setting and as (<reflink idref="bib2" id="ref57">2</reflink>) theoretical predictors of learning outcomes. Moreover, the CoDiL framework differentiates between the external, behavioral side and the internal, cognitive side of a learning activity. The external side of a learning activity encompasses specific student-tool interactions that occur when students work with the implemented digitally enriched instructional features; the internal side of a learning activity encompasses the non-observable mental processes initiating and accompanying the external student actions. Among others, such learning activities relevant for digitally enriched educational settings are the following:</p> <p></p> <ulist> <item> Abstraction: detachment from example-bound or contextualized knowledge to a context-free, generalizable level (Arnon et al., [<reflink idref="bib5" id="ref58">5</reflink>]; Lehtinen &amp; Repo, [<reflink idref="bib56" id="ref59">56</reflink>]; Renkl, [<reflink idref="bib84" id="ref60">84</reflink>]; Rittle-Johnson &amp; Star, [<reflink idref="bib87" id="ref61">87</reflink>]).</item> <p></p> <item> Conducting experiments: engagement in laboratory work to answer a question based on empirically obtained data (Hart et al., [<reflink idref="bib32" id="ref62">32</reflink>]; Lazonder &amp; Harmsen, [<reflink idref="bib55" id="ref63">55</reflink>]; Wörner et al., [<reflink idref="bib107" id="ref64">107</reflink>]).</item> <p></p> <item> Creating examples: generation of different instances of a phenomenon for the purpose of experiencing structure, extending the range of variation, experiencing generality and the constraints and meanings of conventions, or extending example spaces and exploring boundaries (Guerrero et al., [<reflink idref="bib30" id="ref65">30</reflink>]; Watson &amp; Mason, [<reflink idref="bib105" id="ref66">105</reflink>]).</item> <p></p> <item> Exploration: initial active, own examination of the learning objects to activate relevant prior knowledge and to raise questions (M. Lachner et al., [<reflink idref="bib53" id="ref67">53</reflink>]; Loibl et al., [<reflink idref="bib60" id="ref68">60</reflink>]).</item> <p></p> <item> Formulating hypothesis: formation and evaluation of theory to formulate a claim that can be confirmed or refuted following experimental scientific endeavor (Klahr &amp; Dunbar, [<reflink idref="bib46" id="ref69">46</reflink>]; Park, [<reflink idref="bib75" id="ref70">75</reflink>]).</item> <p></p> <item> Refinement: improvement of the quality of knowledge by making it more accurate, appropriately general, or discriminating (Booth et al., [<reflink idref="bib10" id="ref71">10</reflink>]; Koedinger et al., [<reflink idref="bib47" id="ref72">47</reflink>]).</item> <p></p> <item> Revision: fundamental restructuring of the learners' pre-instructional conceptual structures to allow understanding of the intended final knowledge (Chi, [<reflink idref="bib13" id="ref73">13</reflink>]; Duit &amp; Treagust, [<reflink idref="bib19" id="ref74">19</reflink>]; Schroeder &amp; Kucera, [<reflink idref="bib91" id="ref75">91</reflink>]; Vosniadou, [<reflink idref="bib104" id="ref76">104</reflink>]).</item> <p></p> <item> Self-explanation: explaining the new-to-learn content to oneself to achieve deepened processing of the learning materials (Bisra et al., [<reflink idref="bib8" id="ref77">8</reflink>]; Renkl et al., [<reflink idref="bib83" id="ref78">83</reflink>]).</item> </ulist> <p>The cognitive processes that are typically assumed to describe how these learning activities result in the acquisition of knowledge components are selecting, organizing, and integrating (Mayer, [<reflink idref="bib64" id="ref79">64</reflink>]) information to construct new knowledge—which is integrated into the already existing prior knowledge structure of the long-term memory. This integrated knowledge structure allows learners to apply the acquired knowledge in new situations. Internally, these cognitive processes lead to linking the new information to prior knowledge and thereby constructing new knowledge beyond the given information (Mayer, [<reflink idref="bib63" id="ref80">63</reflink>]).</p> <p>Although such learning activities relating to concrete domain-specific content are of key interest for developing learning environments, they are often not specified in detail in educational research (Yeo &amp; Fazio, [<reflink idref="bib108" id="ref81">108</reflink>]), especially when research focuses on learning with digital tools (Hillmayr et al., [<reflink idref="bib36" id="ref82">36</reflink>]; Kucirkova, [<reflink idref="bib50" id="ref83">50</reflink>]). Yet, we consider the understanding of these learning activities necessary to answer the question <emph>why</emph> digital tools work—and what <emph>makes</emph> some learners succeed while others fail. In context of the CoDiL framework, such learning activities function as a "link" between students' external, behavioral interaction with the digital tool and students' internal, cognitive processes that lead to knowledge acquisition (which allows for a theory-driven operationalization of cognitive activities via student-tool-interactions; see the "Evaluation—Linking Students' On-Task Behavior and Learning" section). More specifically, the CoDiL framework understands students' engagement in these learning activities as necessity for successful learning—in line with the ULO framework and generative learning theory (Fiorella &amp; Mayer, [<reflink idref="bib22" id="ref84">22</reflink>]; Roelle &amp; Nückles, [<reflink idref="bib88" id="ref85">88</reflink>]).</p> <hd id="AN0175123852-6">Digital Instruction—Instructional Features as Opportunities Stimulating Learning Activities</hd> <p>When referring to digital instruction, a decisive argument is the media debate initiated by Clark and Kozma (Clark, [<reflink idref="bib16" id="ref86">16</reflink>]; Kozma, [<reflink idref="bib49" id="ref87">49</reflink>]). They argue that on the one hand "media are mere vehicles that deliver instruction but do not influence student achievement" (Clark, [<reflink idref="bib16" id="ref88">16</reflink>], p. 22), but on the other hand that certain media "possess particular characteristics that make them both more and less suitable for the accomplishment of certain kinds of learning tasks" (Kozma, [<reflink idref="bib49" id="ref89">49</reflink>], p. 8). Such 'particular characteristics' have been broadly discussed and investigated since the 1980s, among them ways to direct student attention to learning goals (Scardamalia et al., [<reflink idref="bib89" id="ref90">89</reflink>]) or ways to promote students help-seeking behavior (Aleven et al., [<reflink idref="bib1" id="ref91">1</reflink>])—to name just two. The CoDiL framework acknowledges these perspectives; we argue in line with the media debate that it is not the digital tool <emph>itself</emph> that initiates learning activities (i.e., Clark's argument) but the implemented digitally enriched <emph>instructional features</emph> (i.e., Kozma's argument—the 'particular characteristics').</p> <p>Such instructional features can be regarded as the subject of affordances and constraints, which frame the activity patterns of students and thus the learning activities. Greeno ([<reflink idref="bib27" id="ref92">27</reflink>]) in fact used the notion of affordances and constraints much broader, incorporating social and material dimensions of the learning situation; however, these notions have been also successfully introduced into media design with a focus on the features of the environment which support and guide cognition (e.g., Hartson, [<reflink idref="bib33" id="ref93">33</reflink>]; Norman, [<reflink idref="bib71" id="ref94">71</reflink>]).</p> <p>In context of the CoDiL framework, <emph>instructional features</emph> are understood as (<reflink idref="bib1" id="ref95">1</reflink>) digitally enriched instructional events, which (<reflink idref="bib2" id="ref96">2</reflink>) have the potential to stimulate learning activities; due to them being (<reflink idref="bib3" id="ref97">3</reflink>) subject of affordances and constraints; they should be (<reflink idref="bib4" id="ref98">4</reflink>) designed in order to foster learning activities—i.e., to lower extraneous load (Sweller, [<reflink idref="bib99" id="ref99">99</reflink>]) and to increase generative processing (Schumacher &amp; Stern, [<reflink idref="bib92" id="ref100">92</reflink>]) when compared to non-digitally enriched educational settings. This is in line with Cognitive Theory of Multimedia Learning (CTML; Mayer, [<reflink idref="bib65" id="ref101">65</reflink>]). We consider, among others, the following digitally enriched instructional features as essential:</p> <p></p> <ulist> <item> Adaptive <emph>feedback</emph> (e.g., Aleven et al., [<reflink idref="bib2" id="ref102">2</reflink>]; Reinhold et al., [<reflink idref="bib81" id="ref103">81</reflink>]) may stimulate <emph>refinement</emph> activities, as it may activate prior knowledge and aid discriminating one's potentially too narrowly defined knowledge structures. It may initiate <emph>revision</emph> activities by revealing the limits of students' prior knowledge, when students are not only confronted with the correct solution, but also with their most-likely misconception.</item> <p></p> <item> Opportunities to <emph>try and fail</emph> by posing problems prior to instruction (PS-I; e.g., Boomgaarden et al., [<reflink idref="bib9" id="ref104">9</reflink>]; Kapur, [<reflink idref="bib44" id="ref105">44</reflink>]; Loibl et al., [<reflink idref="bib60" id="ref106">60</reflink>]) may stimulate <emph>exploration</emph> activities. Here digitally enriched learning environments can aid these learning activities by leading students through the problem-solving process and address students' failed attempts to make their misconceptions salient and other conceptions more reasonable (Holmes et al., [<reflink idref="bib38" id="ref107">38</reflink>]).</item> <p></p> <item> Prompting students (e.g., Rau et al., [<reflink idref="bib78" id="ref108">78</reflink>]; Rittle-Johnson et al., [<reflink idref="bib86" id="ref109">86</reflink>]) to use task-specific hints or reconsider specific well-known strategies while solving tasks may stimulate <emph>refinement</emph> activities—as students may engage in overt elaboration (Fiorella &amp; Mayer, [<reflink idref="bib22" id="ref110">22</reflink>]; Weinstein &amp; Mayer, [<reflink idref="bib106" id="ref111">106</reflink>]). While elaboration has been shown to foster sense-making and retention, learners often do not spontaneously engage in elaboration processes, but need external guidance, for instance, by <emph>prompts</emph>, to engage in such processes (Berthold et al., [<reflink idref="bib7" id="ref112">7</reflink>]; Chi et al., [<reflink idref="bib15" id="ref113">15</reflink>]; Endres et al., [<reflink idref="bib20" id="ref114">20</reflink>]; Nückles et al., [<reflink idref="bib73" id="ref115">73</reflink>]).</item> <p></p> <item> Prompting students to summarize and explain central instructional information or draw conclusions from simulations (e.g., Hofer et al., [<reflink idref="bib37" id="ref116">37</reflink>]; Reinhold et al., [<reflink idref="bib80" id="ref117">80</reflink>]) may stimulate <emph>self-explanation</emph> activities, aiding the promotion of active construction of conceptual knowledge.</item> <p></p> <item> Contrasting cases (e.g., Ma et al., [<reflink idref="bib62" id="ref118">62</reflink>]; Schalk et al., [<reflink idref="bib90" id="ref119">90</reflink>]) may stimulate <emph>revision</emph> activities, since learners are encouraged to make connections, identify patterns, and develop their own insights about the topic by comparing two or more examples of a particular concept, issue, or phenomenon—revealing limits of one's own prior knowledge. Dynamic contrasting cases may make the relevant features more salient.</item> </ulist> <p>Of course, some of these (digitally enriched) instructional features can be included in classical (i.e., non-digital) classroom instruction as well, following agreed upon principles of instructional design (Gagne et al., [<reflink idref="bib24" id="ref120">24</reflink>]; Merrill, [<reflink idref="bib66" id="ref121">66</reflink>])—yet, the implementation in digitally enriched environments might be particularly promising since affordances of media-based instructions come into play (Mayer, [<reflink idref="bib65" id="ref122">65</reflink>]; Sweller, [<reflink idref="bib99" id="ref123">99</reflink>]).</p> <hd id="AN0175123852-7">Research on Digitally Enriched Educational Settings Utilizing the CoDiL Framework</hd> <p>As a synthesis of the above-mentioned different theoretical frameworks, the CoDiL framework can serve as a holistic framework guiding both the development of digitally enriched educational settings and research thereof. More precisely, structuring research within the CoDiL framework (in the development of the digital tools <emph>and</emph> the research strategy) aligns research endeavors with (<reflink idref="bib1" id="ref124">1</reflink>) theories of cognition during learning of (<reflink idref="bib2" id="ref125">2</reflink>) a specific content. It theoretically focuses such endeavors on (<reflink idref="bib3" id="ref126">3</reflink>) the assumed underlying learning activities which the (<reflink idref="bib4" id="ref127">4</reflink>) digitally enriched instructional features should enhance. Thus, it may help researchers to establish (<reflink idref="bib5" id="ref128">5</reflink>) a link between students' cognition and their on-task behavior which allows for testing causal effects of <emph>why</emph> specific features of digital tools are beneficial for learning.</p> <hd id="AN0175123852-8">Development—Designing Digitally Enriched Educational Settings</hd> <p>In line with generative learning theory (Fiorella &amp; Mayer, [<reflink idref="bib22" id="ref129">22</reflink>]; Roelle &amp; Nückles, [<reflink idref="bib88" id="ref130">88</reflink>]) and theories about self-regulation (Azevedo, [<reflink idref="bib6" id="ref131">6</reflink>]; S. Li et al., [<reflink idref="bib58" id="ref132">58</reflink>]; Molenaar et al., [<reflink idref="bib68" id="ref133">68</reflink>]), the CoDiL framework highlights the mediating role of students' engagement in specific learning activities in the cause-and-effect mechanisms of successful learning with digital tools. That is, developing specific digital tools for educational purposes includes the design of digitally enriched instructional features which bear the potential to stimulate learning activities which—mediated by cognitive processes resulting from those learning activities—lead to knowledge acquisition (Fig. 1). By combining and linking content-specific theories of learning with theories of what stimulates relevant learning activities, the CoDiL framework aims at highlighting the following design principles: in order to find the appropriate to-be-implemented instructional features, knowledge about content-specific learner models (i.e., knowledge components and learning activities) should be used to design educational settings (i.e., implement instructional features in the digital tool) that are well-suited to stimulate the relevant generative cognitive processes. The rather broad synthetic structure of the CoDiL framework may serve as a guideline for finding an appropriate theoretical foundation and may illustrate aspects to consider in developmental steps. This may be demanding as it can vary between domains, content, and learning goals, as described in the following different examples, which highlight different aspects of this endeavor:</p> <p></p> <ulist> <item> Consider developing an online math tutor to support students in learning fraction arithmetic. Keeping the relevant theoretical mediator for learning success in mind (i.e., engagement in abstraction and refinement activities), digitally enriched instructional features selected to be implemented should bear the potential to <emph>leverage</emph> students' knowledge about how to operate with fractions (i.e., to come to more accurate and faster solutions when applying arithmetic operations and to appropriately generate conceptual understanding of those operations) during learning with the tool. For that, adaptive feedback and an adaptive increase in task difficulty regarding difficulty-generating factors may seem appropriate (Reinhold et al., [<reflink idref="bib81" id="ref134">81</reflink>]).</item> <p></p> <item> In contrast, when developing an appropriate digitally enriched educational setting for students to learn about a physical phenomenon, digitally enriched instructional features should support the a-priori identified purposeful reasoning processes that may establish relevant knowledge facets: to stimulate experimental activities (e.g., formulating hypothesis, conducting experiments) leading to a conceptual understanding of the "control of variables strategy," a simulation enabling that strategy in a salient way may be an appropriate digitally enriched instructional feature (e.g., Greiff et al., [<reflink idref="bib28" id="ref135">28</reflink>]; see also scientific discovery as dual search, Künsting et al., [<reflink idref="bib51" id="ref136">51</reflink>]).</item> </ulist> <hd id="AN0175123852-9">Evaluation—Linking Students' On-Task Behavior and Learning</hd> <p>Studies in the field of digitally enriched learning often focus on the direct path, i.e., the effect of digital tools on learning outcome when compared to another learning scenario—which allows only limited interpretation and explanation of changes in learning outcomes. Following and combining theories of learning (e.g., Fiorella &amp; Mayer, [<reflink idref="bib22" id="ref137">22</reflink>]; Mayer, [<reflink idref="bib65" id="ref138">65</reflink>]), the CoDiL framework is based on the identical premises that learning activities (and the cognitive processes they result in) during (digital) learning—stimulated and triggered by constraints and affordances that go along with specific digitally enriched instructional features—are what contributes to the final learning outcome. For this reason, we consider the external, behavioral side of students' learning activities as theoretical as well as empirical mediators of achievement.</p> <p>In this section, we illustrate how the relevant theories about linking behavioral data to student cognition inform the CoDiL framework in how to investigate these activities directly (and as unobtrusively as possible) during learning: following up on the argument that implemented instructional features in digital tools can stimulate learning activities, students' <emph>interaction with these features</emph> (external, behavioral side of the learning activities, implying any process data indicators for student-tool-interaction that can be recorded, e.g., click behavior, or writing-to-learn text prompts, to name two) can be a valid indicator for the <emph>cognitive side of learning activities</emph> (internal, latent, and thought to result in knowledge acquisition, mediated by cognitive processes).</p> <p>There is a broad variety of different conceptual ideas for how to establish such a link between behavior and cognition on a theoretical and empirical level (Goldhammer et al., [<reflink idref="bib25" id="ref139">25</reflink>]; Greiff et al., [<reflink idref="bib28" id="ref140">28</reflink>]; Huber &amp; Bannert, [<reflink idref="bib40" id="ref141">40</reflink>]; Mislevy et al., [<reflink idref="bib67" id="ref142">67</reflink>]; Molenaar et al., [<reflink idref="bib68" id="ref143">68</reflink>]; Sedrakyan et al., [<reflink idref="bib94" id="ref144">94</reflink>]). For the CoDiL framework to serve as a widely open framework aiming at causal interpretation of learning effects, we aimed at compatibility with a large variety of different analytical approaches. This is why we consider the term "process data" (as a broader term than "log data") as an umbrella term encompassing (i) student-tool interactions logged unobtrusively by the digital tool itself and (ii) other process indicators obtained from accompanying assessments during learning, such as, finger-tracing data in open-learning environments (e.g., Moyer-Packenham et al., [<reflink idref="bib70" id="ref145">70</reflink>]; Zuo &amp; Lin, [<reflink idref="bib109" id="ref146">109</reflink>]), solutions from rather closed unique cognitive items inside a digital learning path (e.g., Boomgaarden et al., [<reflink idref="bib9" id="ref147">9</reflink>]; Rau et al., [<reflink idref="bib79" id="ref148">79</reflink>]), eye-tracking data (Nückles, [<reflink idref="bib72" id="ref149">72</reflink>]; Strohmaier et al., [<reflink idref="bib98" id="ref150">98</reflink>]), think-aloud protocols accompanying the use of any kind of digital tool (Ericsson &amp; Simon, [<reflink idref="bib21" id="ref151">21</reflink>]; A. Lachner &amp; Nückles, [<reflink idref="bib52" id="ref152">52</reflink>]; Renkl, [<reflink idref="bib82" id="ref153">82</reflink>]), and journal writing during self-regulated learning with digital tools (Nückles et al., [<reflink idref="bib74" id="ref154">74</reflink>]). To sum up, the CoDiL framework emphasizes widely agreed-upon necessities for establishing a link between process data and hypothesized cognition (Goldhammer et al., [<reflink idref="bib25" id="ref155">25</reflink>]; Huber &amp; Bannert, [<reflink idref="bib40" id="ref156">40</reflink>]; Molenaar et al., [<reflink idref="bib68" id="ref157">68</reflink>]; Sedrakyan et al., [<reflink idref="bib94" id="ref158">94</reflink>])—i.e., the need for (a) a solid theoretical foundation, as well as (b) appropriate indicators inside the process data files—to allow for differentiated statements about the effects of digital tools (or more specifically, their implemented instructional features) on learning success. For example,</p> <p></p> <ulist> <item> Consider Lalley and colleagues' ([<reflink idref="bib54" id="ref159">54</reflink>]) study on the comparison of virtual vs. real frog dissection in biology classrooms. The theoretical framework takes into account ethical and health issues, as well as specimen decay—underlining the necessity of virtual alternatives to reach the same learning goals and asking whether "virtual dissection procedure [result in] comparable learning ... outcomes when compared to traditional dissection" (p. 191). While the authors could show a positive effect of the digital learning tool provided compared to real dissection, this effect cannot be further evaluated within the theoretical framework of the study, as no hypotheses about the digitally enriched instructional features implemented in the software and their relation to learning activities during the virtual dissection (compared to the real dissection) are stated. Thus, the research framework lacks a theoretical mediator that could explain why the virtual approach is more effective than the traditional approach—leaving essential questions unanswered.</item> <p></p> <item> In clear contrast, the study by Koedinger and colleagues ([<reflink idref="bib48" id="ref160">48</reflink>]) is prototypical for the relevant elements of the CoDiL framework. Here, students who use a formative assessment mathematics tool (and students who do not) were compared in terms of learning outcomes measured via a standardized mathematics test. The theoretical framework (a) encompasses well-established effects of adaptive and individualized learning in the context of formative assessment, stating explicitly that "students would benefit from the tutoring, feedback, and [the] design of the ... system" (p. 496)—routed down to estimated positive effects of "practice with timely feedback" and "individualized tutorial assistance" (p. 496). Thus, adaptive immediate feedback and individualized tools (which would be considered digitally enriched instructional features providing additional learning opportunities in terms of the CoDiL framework)—and not digital tool use in general—are regarded as the cause for the effect on learning. From the perspective of the CoDiL framework, these instructional features may stimulate <emph>refinement</emph> or <emph>revision</emph> activities in students (i.e., the theoretical mediator of the effect of the digital tool). Yet, for them to become predictors of achievement, it is relevant if (or to which amount) students make use of these digitally enriched instructional features—shifting from design features of the digital tool to the above-mentioned specific learning activities students actually engage in while using the digitally enriched learning tool. Koedinger et al. ([<reflink idref="bib48" id="ref161">48</reflink>]) assessed engagement in these learning activities by behavioral measures of student-tool interaction; (b) students who did not complete a certain amount of items in the tool were labeled "low usage" and students who did were labeled "high usage"—shedding light on an interaction effect in line with their hypotheses.</item> </ulist> <hd id="AN0175123852-10">Discussion</hd> <p>In this article, we summarized and reviewed different existing theoretical and empirical works regarding the role of digital tools in educational settings. We presented the CoDiL framework as a synthesis of different positions about that specific role of digital tools. One main argument from the last decades of research on educational technology which we want to emphasize is that cognitive processes have a mediating role in the cause-and-effect mechanisms of successful subject-specific learning with digital tools. This mediating role can be framed both in a theoretical and an empirical manner: on a theoretical level, the CoDiL framework describes digital tools by focusing on their implemented digitally enriched instructional features. These instructional features offer learning opportunities to students that may (or may not) be utilized; if utilized they trigger learning activities—which mediated by cognitive processes and in interaction with prior knowledge lead to knowledge gains. From an empirical perspective, these learning activities should be operationalizable by measurable student-tool interactions (generating a broad variety of different process data). Yet, we argue that for the framework to be able to guide research on digital tools in education, its 'degrees of freedom' need to be specified for the concrete educational scenario of interest: we recognize that our current framework represents a high-level conceptual integration of KLI, SOI, and ULO. This provides a basic starting point, but more detailed and empirical integration into specific (newly conducted) studies is required. This offers potential for further research from various fields.</p> <hd id="AN0175123852-11">On the Assessment of Process Data During Learning With Digital Tools</hd> <p>Present theoretical frameworks on learning in digitally enriched or multimedia scenarios (Chi &amp; Wylie, [<reflink idref="bib14" id="ref162">14</reflink>]; Järvelä, [<reflink idref="bib41" id="ref163">41</reflink>]; Mayer, [<reflink idref="bib65" id="ref164">65</reflink>]; Sweller, [<reflink idref="bib99" id="ref165">99</reflink>]) are based on the premise that cognitive processes during (digital) learning cause knowledge acquisition. Although this can be considered a commonly agreed-upon statement about how learning occurs, the specific underlying (latent) learning processes are not always operationalized and assessed in studies about learning in digitally enriched scenarios. Consequently, some recent publications call for a more concrete investigation of the learning process (de Jong, [<reflink idref="bib18" id="ref166">18</reflink>]; Kucirkova, [<reflink idref="bib50" id="ref167">50</reflink>]) to gain insight into the mechanisms explaining the effects of digital-tool design (Boomgaarden et al., [<reflink idref="bib9" id="ref168">9</reflink>]; Sedrakyan et al., [<reflink idref="bib94" id="ref169">94</reflink>])—or aptitude-treatment interactions (Grimm et al., [<reflink idref="bib29" id="ref170">29</reflink>]; Huber &amp; Bannert, [<reflink idref="bib40" id="ref171">40</reflink>]; Reinhold et al., [<reflink idref="bib81" id="ref172">81</reflink>]). In order to reflect this in our synthesis model, we have centered the CoDiL framework on learning activities that have shown relevant to (digital) learning (e.g., abstraction, conducting experiments, creating examples, exploration, formulating hypothesis, refinement, revision, self-explanation). As this is an active process (in accordance with generative learning theory, Fiorella &amp; Mayer, [<reflink idref="bib22" id="ref173">22</reflink>]; Roelle &amp; Nückles, [<reflink idref="bib88" id="ref174">88</reflink>]; and theories about self-regulation, Azevedo, [<reflink idref="bib6" id="ref175">6</reflink>]; S. Li et al., [<reflink idref="bib58" id="ref176">58</reflink>]; Molenaar et al., [<reflink idref="bib68" id="ref177">68</reflink>]), we integrated ULO in our framework to contribute for various student behavior (Seidel, [<reflink idref="bib95" id="ref178">95</reflink>]); as a direct result of the presented arguments, students' engagement in these learning activities serves as a theoretical as well as an <emph>empirical</emph> mediator in the CoDiL framework. Regarding research on the role of digital tools in education, one main work for future primary research is to develop various ways to unobtrusively capture student-tool interactions—and to link these behaviors to the underlying cognitive processes validly. Given such valid measures of learning activities in digitally enriched educational settings, mediation analyses could empirically underpin the role of these activities as theoretically grounded causes for the effects of learning with digital tools.</p> <hd id="AN0175123852-12">On the Development of Digital Tools for Educational Purposes</hd> <p>We consider the design of the digital tool (and its digitally enriched instructional features) a central part of the development of a digitally enriched learning environment: we consider it relevant to start research on digitally enriched learning with analyses of the to-be-learned content, the necessary knowledge elements, and the required learning activities to achieve these learning goals—and, thus, <emph>not</emph> by picking a to-be-investigated digital tool first. In fact, we consider it necessary for researchers to engage in the development (or selection) of digital prototype tools that are <emph>explicitly designed</emph> to align with the learning mechanisms assumed. Given that, the link between students' underlying learning activities and their behavioral student-tool interactions may be established during tool development (Goldhammer et al., [<reflink idref="bib25" id="ref179">25</reflink>]).</p> <hd id="AN0175123852-13">On the Role of Content-Specificity in Learning With Digital Tools</hd> <p>Whereas multiple aspects are important to be considered as mediators for learning success (e.g., cognitive load; Sweller et al., [<reflink idref="bib100" id="ref180">100</reflink>]), the activation of and engagement in specific learning activities is considered to be of particular relevance (e.g., Koedinger et al., [<reflink idref="bib47" id="ref181">47</reflink>]; Schumacher &amp; Stern, [<reflink idref="bib92" id="ref182">92</reflink>]). By describing the CoDiL framework, we argue that digital tools should be designed to stimulate such learning activities. In line with Koedinger and colleagues' KLI framework, we consider particular purposeful learning activities (that result in cognitive processes relevant for knowledge acquisition) to be <emph>content-specific</emph> (Koedinger et al., [<reflink idref="bib47" id="ref183">47</reflink>]). In our framework, we stress the need to provide detailed information on the specific knowledge components that students need to acquire to learn a specific content. Based on this specification of knowledge components, digital tools can be developed to not only support learning <emph>in general</emph> (e.g., meta-cognitive scaffolding or corrective feedback), but also to stimulate the essential <emph>content-specific</emph> (conceptual) knowledge components itself (e.g., task-specific feedback on the most plausible student error given the students' answer or relevant and hierarchically ordered contrasting cases). Thus, the digital tool (and its implemented instructional features) should be considered an inherent part of the learning environment—aiming for a concrete learning goal.</p> <hd id="AN0175123852-14">Conclusion: Guiding Research on Digital Tools in Educational Settings</hd> <p>Studies in the field of digital learning often focus on the investigation of direct effects of digital tools on students' learning outcome. With this article, we provide a theoretical framework of the learning activities <emph>mediating</emph> learning with such digital tools in educational settings—focusing on the underlying learning mechanisms in digitally enriched learning scenarios. Research on digital tools in educational settings guided by the proposed CoDiL framework should follow these steps:</p> <p></p> <ulist> <item> Specify the content-specific knowledge components to be learned.</item> <p></p> <item> Specify the learning activities that lead to the acquisition of these knowledge components.</item> <p></p> <item> Select or design digital tools with instructional features that stimulate and support these learning activities.</item> <p></p> <item> Collect process data (e.g., on student-tool interaction) to measure the actual utilization of the digitally enriched instructional features of the digital tool that may serve as indicators for the cognitive processes taking place.</item> </ulist> <hd id="AN0175123852-15">Funding</hd> <p>Open Access funding enabled and organized by Projekt DEAL. 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| Header | DbId: eric DbLabel: ERIC An: EJ1409433 AccessLevel: 3 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Learning Mechanisms Explaining Learning with Digital Tools in Educational Settings: A Cognitive Process Framework – Name: Language Label: Language Group: Lang Data: English – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Frank+Reinhold%22">Frank Reinhold</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0003-4468-024X">0000-0003-4468-024X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Timo+Leuders%22">Timo Leuders</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-7621-7826">0000-0002-7621-7826</externalLink>)<br /><searchLink fieldCode="AR" term="%22Katharina+Loibl%22">Katharina Loibl</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0002-1773-1913">0000-0002-1773-1913</externalLink>)<br /><searchLink fieldCode="AR" term="%22Matthias+Nückles%22">Matthias Nückles</searchLink><br /><searchLink fieldCode="AR" term="%22Maik+Beege%22">Maik Beege</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5335-3174">0000-0001-5335-3174</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jan+M%2E+Boelmann%22">Jan M. Boelmann</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="SO" term="%22Educational+Psychology+Review%22"><i>Educational Psychology Review</i></searchLink>. 2024 36(1). – Name: Avail Label: Availability Group: Avail Data: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/ – Name: PeerReviewed Label: Peer Reviewed Group: SrcInfo Data: Y – Name: Pages Label: Page Count Group: Src Data: 21 – Name: DatePubCY Label: Publication Date Group: Date Data: 2024 – Name: TypeDocument Label: Document Type Group: TypDoc Data: Journal Articles<br />Reports - Descriptive – Name: Subject Label: Descriptors Group: Su Data: <searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Intellectual+Disciplines%22">Intellectual Disciplines</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Activities%22">Learning Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Instructional+Design%22">Instructional Design</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Psychology%22">Cognitive Psychology</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Psychology%22">Educational Psychology</searchLink> – Name: DOI Label: DOI Group: ID Data: 10.1007/s10648-024-09845-6 – Name: ISSN Label: ISSN Group: ISSN Data: 1040-726X<br />1573-336X – Name: Abstract Label: Abstract Group: Ab Data: To explain successful subject matter learning with digital tools, the specification of mediating cognitive processes is crucial for any empirical investigation. We introduce a cognitive process framework for the mechanisms of learning with digital tools (CoDiL) that combines core ideas from the psychology of instruction (utilization-of-learning-opportunity framework), cognitive psychology (knowledge-learning-instruction framework), and domain-specific research on learning and instruction. This synthesizing framework can be used to theoretically ground, firstly, the design of digital tools for learning, and secondly, the empirical analysis of students' learning activities in digitally enriched educational settings via the analysis of specific student-tool interactions. – Name: AbstractInfo Label: Abstractor Group: Ab Data: As Provided – Name: DateEntry Label: Entry Date Group: Date Data: 2024 – Name: AN Label: Accession Number Group: ID Data: EJ1409433 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10648-024-09845-6 Languages: – Text: English PhysicalDescription: Pagination: PageCount: 21 Subjects: – SubjectFull: Cognitive Processes Type: general – SubjectFull: Intellectual Disciplines Type: general – SubjectFull: Learning Activities Type: general – SubjectFull: Technology Uses in Education Type: general – SubjectFull: Instructional Design Type: general – SubjectFull: Cognitive Psychology Type: general – SubjectFull: Teaching Methods Type: general – SubjectFull: Educational Psychology Type: general Titles: – TitleFull: Learning Mechanisms Explaining Learning with Digital Tools in Educational Settings: A Cognitive Process Framework Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Frank Reinhold – PersonEntity: Name: NameFull: Timo Leuders – PersonEntity: Name: NameFull: Katharina Loibl – PersonEntity: Name: NameFull: Matthias Nückles – PersonEntity: Name: NameFull: Maik Beege – PersonEntity: Name: NameFull: Jan M. Boelmann IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 1040-726X – Type: issn-electronic Value: 1573-336X Numbering: – Type: volume Value: 36 – Type: issue Value: 1 Titles: – TitleFull: Educational Psychology Review Type: main |
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