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Exploring Autonomous Learning Capacity from a Self-Regulated Learning Perspective Using Learning Analytics

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Title: Exploring Autonomous Learning Capacity from a Self-Regulated Learning Perspective Using Learning Analytics
Language: English
Authors: Papamitsiou, Zacharoula (ORCID 0000-0002-0982-3623), Economides, Anastasios A.
Source: British Journal of Educational Technology. Nov 2019 50(6):3138-3155.
Availability: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
Peer Reviewed: Y
Page Count: 18
Publication Date: 2019
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Learning Analytics, Independent Study, Personal Autonomy, Learning Strategies, Decision Making, Undergraduate Students, Goal Orientation, Help Seeking, Time Management
DOI: 10.1111/bjet.12747
ISSN: 0007-1013
Abstract: Practising self-regulated learning (SRL) has been proposed to develop learning autonomy. However, there is lack of empirical evidence on how SRL strategies affect autonomous learning capacity. This study attempts to bridge that gap by utilizing the learners' trace data for measuring the learners' autonomous interactions, and investigates the effects of four SRL strategies on learners' autonomous choices. The goal is to explain how the employed SRL strategies impact autonomous control (in terms of frequencies of self-enforced decisions, as well as time-spent on decision making). The results from an exploratory study with undergraduate learners (N = 113) shown that goal-setting and time-management have strong positive effects on autonomous control, effort-regulation moderately positively affects learners' autonomy, while help-seeking has a strong negative effect. These findings provide empirical evidence and contribute to clarifying the role of each one of the SRL strategies in the development of autonomous learning capacity, from a learning analytics perspective. Limitations and potential implications for research and practice are also discussed.
Abstractor: As Provided
Entry Date: 2019
Accession Number: EJ1232163
Database: ERIC
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  Value: <anid>AN0139230530;58i01nov.19;2019Oct23.02:46;v2.2.500</anid> <title id="AN0139230530-1">Exploring autonomous learning capacity from a self‐regulated learning perspective using learning analytics </title> <p>Practising self‐regulated learning (SRL) has been proposed to develop learning autonomy. However, there is lack of empirical evidence on how SRL strategies affect autonomous learning capacity. This study attempts to bridge that gap by utilizing the learners' trace data for measuring the learners' autonomous interactions, and investigates the effects of four SRL strategies on learners' autonomous choices. The goal is to explain how the employed SRL strategies impact autonomous control (in terms of frequencies of self‐enforced decisions, as well as time‐spent on decision making). The results from an exploratory study with undergraduate learners (N = 113) shown that goal‐setting and time‐management have strong positive effects on autonomous control, effort‐regulation moderately positively affects learners' autonomy, while help‐seeking has a strong negative effect. These findings provide empirical evidence and contribute to clarifying the role of each one of the SRL strategies in the development of autonomous learning capacity, from a learning analytics perspective. Limitations and potential implications for research and practice are also discussed.</p> <p></p> <ulist> <item> Autonomous learning is more possible to be efficient because it encompasses the learner's freedom of choice and control over learning, according to what is important to the learner herself.</item> <p></p> <item> Self‐regulated learning is a process comprising of strategies that can be trained, while autonomy is a psychological need for experiencing volition and is difficult to be trained.</item> <p></p> <item> Empirical research on how learning strategies affect the development of capacity for autonomous learning is based solely on learners' self‐reported perceptions and not on measurements of autonomous interactions.</item> <p></p> <item> The current study takes us a step ahead by considering the learners' autonomous choices and provides an example of modeling autonomy using learning analytics.</item> <p></p> <item> The moderate effect of self‐regulated learning strategies on autonomous choices contributes to understanding that exercising self‐regulation is not enough for becoming autonomous learner.</item> <p></p> <item> This study opens the discussion on how to measure autonomous interactions, how autonomous learning capacity can be developed, and how autonomous learning capacity development can be assessed.</item> <p></p> <item> Practitioners shall be able to design and integrate specific features into online learning environments, that allow for autonomous choices, in a controlled manner, to train and guide learners towards effectively using the self‐regulated learning strategies.</item> <p></p> <item> Learners shall have opportunities to exercise control over their learning towards aligning their own self‐set learning goals with their achievements.</item> <p></p> <item> The role of other traits (eg, personality) and strategies (eg, autonomy‐supportive teaching) should be explored in the scope of understanding learning autonomy.</item> </ulist> <p>Practitioner NotesWhat is already known about this topic What this paper adds Implications for practice and/or policy</p> <hd id="AN0139230530-2">Introduction</hd> <p>Mariana is an electrical engineer professional. Since her early high‐school years, she was interested in astrophysics and the universe science. She was encouraged by her teachers to determine her learning goals in line with her values and interests, and to control and regulate her behavior and actions accordingly, for attaining those goals. As such, she took some relevant courses during her university studies and she reads books and articles online. Recently, she enrolled herself in a Massive Open Online Course (MOOC) related to cosmology, motivated by her own interest on the topic. Rather than merely "reacting" to situations that provide her with the opportunity to learn (eg, responding to teacher‐controlled instruction), Mariana "proactively" seeks out knowledge: she sets her learning goals, regulates her efforts, allocates study‐time, monitors her progress, seeks help when needed, critically reflects on her learning, and gradually, becomes aware of her learning needs. In the open online learning environment of the MOOC, Mariana has the overall control of her learning trajectory: from the selection of the topic, to the selection of the course, and to the degree of completion of the course, according to the goals she has set for herself, Mariana systematically specifies and tracks her path.</p> <p>In this example, the learner is an autonomous and self‐regulated learner. When the learner is autonomous, the learning is possible to be efficient because it is <emph>primarily</emph> important to herself. Central to Self‐Determination Theory (SDT; Deci & Ryan, [<reflink idref="bib10" id="ref1">10</reflink>]) is the notion that autonomy relates to greater learning benefits. The role of autonomy on motivating learner to acquire self‐regulation skills has been extensively explored in literature (eg, Lüftenegger <emph>et al.</emph>, [<reflink idref="bib25" id="ref2">25</reflink>]; Pintrich, [<reflink idref="bib36" id="ref3">36</reflink>]; Reeve, [<reflink idref="bib41" id="ref4">41</reflink>]; Wolters, [<reflink idref="bib57" id="ref5">57</reflink>]). However, <emph>how</emph> learners develop their autonomous learning capacity is still a "black box." The adoption of autonomy‐supportive environments that provide learners the opportunity to exercise self‐regulation strategies might contribute to increasing the efficiency of autonomous choices (eg, Andrade, [<reflink idref="bib1" id="ref6">1</reflink>]; Benson, [<reflink idref="bib4" id="ref7">4</reflink>]; Jossberger, Brand‐Gruwel, Boshuizen, & van de Wiel, [<reflink idref="bib18" id="ref8">18</reflink>]; Loyens, Magda, & Rikers, [<reflink idref="bib24" id="ref9">24</reflink>]). This study empirically explores how specific self‐regulated learning strategies (ie, goal‐setting, effort regulation, help‐seeking and time‐management) are externalized as autonomous interactions within an online learning environment that allows freedom of choice (selection of tasks), using learning analytics.</p> <hd id="AN0139230530-3">Bringing self‐regulated learning and autonomous learning on the same page: similarities, diff...</hd> <p>Mariana is a <emph>self‐regulated learner</emph>. Self‐regulated learning (SRL) is conceptualized as an "active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behavior, guided and constrained by their goals and the contextual features in the environment" (Pintrich, [<reflink idref="bib36" id="ref10">36</reflink>], p. 435). Self‐regulated learners are aware of their learning processes and adjust their behavior (self‐corrections) to keep themselves on track towards their desired outcomes (Carver & Scheier, [<reflink idref="bib7" id="ref11">7</reflink>]; Pintrich, [<reflink idref="bib37" id="ref12">37</reflink>]; Reeve, Ryan, Deci, & Jang, [<reflink idref="bib43" id="ref13">43</reflink>]). SRL is guided by motivation, metacognition, strategic action (planning, monitoring and evaluation), and the specifications of the learning environments (McCardle & Hadwin, [<reflink idref="bib27" id="ref14">27</reflink>]; Pintrich, [<reflink idref="bib36" id="ref15">36</reflink>]; Zimmerman, [<reflink idref="bib60" id="ref16">60</reflink>]).</p> <p>Mariana is also an <emph>autonomous learner</emph>. For Holec ([<reflink idref="bib15" id="ref17">15</reflink>], p. 3), autonomous learning is the "capacity to take charge of one's own learning." This definition assumes accepting responsibility over all spectrum of the learning process, regardless of the learning context or the specifications of the learning environment. Recently, Huang and Benson ([<reflink idref="bib16" id="ref18">16</reflink>]) argued that capacity to control learning comprises ability (knowledge and skills to plan, monitor, evaluate learning), desire (motivation, volition, willingness) and freedom (permission to control).</p> <p>Based on the generic definitions of autonomous learning and SRL <emph>per se</emph>, one gets the impression that both terms point to a significant notional overlap: both concepts emphasize learners' active engagement, goal‐directed behavior, control, metacognition and responsibility. Intrinsic motivation is also prominent in both (Deci & Ryan, [<reflink idref="bib10" id="ref19">10</reflink>]). Although these two terms somehow conflate, they should not be confused with each other. Few studies attempted to bring these concepts on the same page and focused on identifying their similarities, differences and where they intersect (eg, Andrade & Bunker, [<reflink idref="bib2" id="ref20">2</reflink>]; Cosnefroy & Carré, [<reflink idref="bib9" id="ref21">9</reflink>]; Lewis & Vialleton, [<reflink idref="bib22" id="ref22">22</reflink>]; Loyens <emph>et al.</emph>, [<reflink idref="bib24" id="ref23">24</reflink>]).</p> <p>Autonomy is a <emph>psychological need</emph> tied to the learners' sense of volition and control. Core to the idea of autonomy is freedom of choice (Andrade & Bunker, [<reflink idref="bib2" id="ref24">2</reflink>]; Loyens <emph>et al.</emph>, [<reflink idref="bib24" id="ref25">24</reflink>]; Pintrich & Schunk, [<reflink idref="bib38" id="ref26">38</reflink>]), placing the learner at the outset of the learning task. On the contrary, SRL emerged from educational psychology research informed by social cognitive theory and is a <emph>process</emph> comprising a set of strategies and placing emphasis on how learner can be effective without reliance on teacher structure/control (Reeve, [<reflink idref="bib42" id="ref27">42</reflink>]; Stefanou <emph>et al.</emph>, [<reflink idref="bib48" id="ref28">48</reflink>]; Vansteenkiste, <emph>et al.</emph>, [<reflink idref="bib55" id="ref29">55</reflink>]). Furthermore, autonomy targets at fostering learners' responsible self‐initiative and allows them to determine the selection of <emph>what shall be learned</emph>, as well as the critical evaluation (reflection) of the learning tasks that were selected (Candy, [<reflink idref="bib6" id="ref30">6</reflink>]): autonomous learner is able to define <emph>what needs to be learned</emph>. On the other hand, SRL seems more concerned with the <emph>subsequent steps</emph> in the learning process, such as setting goals, monitoring their progress, reflecting on the steps that were taken and changing their plans accordingly. Self‐regulated learner plans the learning activity and enacts tactics and strategies before initiating the learning <emph>task</emph>. SRL is described as the processes that the learner substantiates (Zimmerman, [<reflink idref="bib59" id="ref31">59</reflink>]).</p> <hd id="AN0139230530-4">The need to examine autonomous learning from a self‐regulated learning perspective</hd> <p>When the learner is self‐regulated, the learning is very likely to be efficient: self‐regulation has been associated with deeper and more permanent learning (eg, Broadbent & Poon, [<reflink idref="bib5" id="ref32">5</reflink>]; Pintrich, [<reflink idref="bib37" id="ref33">37</reflink>]; Tsai, Shen, & Tsai, [<reflink idref="bib53" id="ref34">53</reflink>]; Wang, Shannon, & Ross, [<reflink idref="bib56" id="ref35">56</reflink>]). For one to become efficient self‐regulated learner, the role of autonomous motivation has been extensively explored in literature and is beyond question (eg, Lüftenegger <emph>et al.</emph>, [<reflink idref="bib25" id="ref36">25</reflink>]; Maldonado‐Mahauad <emph>et al.</emph>, [<reflink idref="bib26" id="ref37">26</reflink>]; Pintrich, [<reflink idref="bib36" id="ref38">36</reflink>]; Reeve <emph>et al.</emph>, [<reflink idref="bib43" id="ref39">43</reflink>]; Wolters, [<reflink idref="bib57" id="ref40">57</reflink>]). Learners can acquire and improve self‐regulation skills, through guidance and practise (Dignath, Buettner, & Langfeldt, [<reflink idref="bib11" id="ref41">11</reflink>]) in learning environments that promote self‐initiative and support autonomous motivation (Pintrich, [<reflink idref="bib36" id="ref42">36</reflink>]). But first, learners should develop their autonomous capacity.</p> <p>However, learners do not intuitively know how to build their autonomous learning capacity and make efficient learning choices; <emph>autonomy is not a skill, </emph>and therefore, it is difficult to be "trained." Within SDT, autonomy is perceived as the inner endorsement of one's behavior, ie, a <emph>psychological need</emph> for experiencing volition (Deci & Ryan, [<reflink idref="bib10" id="ref43">10</reflink>]). All learners are potentially autonomous, but (a) the degree of autonomy each learner achieves depends on the characteristics of the learner (eg, personality; Olesen, Thomsen, Schnieber, & Tønnesvang, [<reflink idref="bib32" id="ref44">32</reflink>]), and (b) the learner who lacks autonomy can developing it under appropriate conditions (Benson, [<reflink idref="bib4" id="ref45">4</reflink>]).</p> <p>Mariana <emph>was not always</emph> an autonomous learner; she was encouraged by her teachers to take responsibility and control of the learning choices according to her values. For Little ([<reflink idref="bib23" id="ref46">23</reflink>]), autonomy does not imply learning in isolation and complete lack of support, but rather interdependence among teachers and learners. Autonomy‐supportive teaching and autonomy‐supportive design of learning environments have been proposed to increase autonomous motivation, engagement and self‐regulation (eg, Andrade, [<reflink idref="bib1" id="ref47">1</reflink>]; Jossberger <emph>et al.</emph>, [<reflink idref="bib18" id="ref48">18</reflink>]; Reeve, [<reflink idref="bib41" id="ref49">41</reflink>]; Sun & Rueda, [<reflink idref="bib50" id="ref50">50</reflink>]; Vansteenkiste, Simons, Lens, Sheldon, & Deci, [<reflink idref="bib54" id="ref51">54</reflink>]). In these conditions, learners have more freedom to pursue their goals and undertake critical evaluation of the materials they select. It has also been argued that one needs to exercise self‐regulation within appropriately configured environments to become a capable autonomous learner (eg, Andrade, [<reflink idref="bib1" id="ref52">1</reflink>]; Benson, [<reflink idref="bib4" id="ref53">4</reflink>]; Jossberger <emph>et al.</emph>, [<reflink idref="bib18" id="ref54">18</reflink>]; Loyens <emph>et al.</emph>, [<reflink idref="bib24" id="ref55">24</reflink>]). In other words, mastering the skills to self‐regulate activities and tasks can be the first step to learning how to become autonomous.</p> <p>Apparently, it is very likely that self‐regulation might affect the self‐enforced choices in terms of interactions measured with learning analytics parameters: the question is <emph>how</emph>. <emph>How</emph> SRL strategies contribute to developing learning autonomy is still a "black box." It is critical to examine how SRL strategies are externalized as learners' autonomous choices, in learning environments that allow learners to be consciously involved in their own learning by supporting freedom of choices, and to investigate autonomous interactions from the SRL perspective.</p> <hd id="AN0139230530-5">Autonomous learning and SRL strategies in online learning environments</hd> <p>The more the learning turns online, the higher is the need for learners to develop and sustain their autonomous learning trait. The reason behind this claim is that contemporary online learning environments provide learners more opportunities to freely choose what, where and how to learn, eliminating the restriction of place, time and physical materials and, up‐to a degree, giving the learners substantial control of their learning (compared to traditional classrooms or to blended learning environments) (Broadbent & Poon, [<reflink idref="bib5" id="ref56">5</reflink>]; Xu & Jaggars, [<reflink idref="bib58" id="ref57">58</reflink>]). These environments inherently promote learners' active engagement in the learning process and at the same time require learners' abilities to manage their own learning processes (Sun & Rueda, [<reflink idref="bib50" id="ref58">50</reflink>]; Tsai <emph>et al.</emph>, [<reflink idref="bib53" id="ref59">53</reflink>]) by providing less support and guidance on how to efficiently and deeply learn (Wang <emph>et al.</emph>, [<reflink idref="bib56" id="ref60">56</reflink>]). In order learners to be(come) capable to exploit the opportunities of online learning environments to efficiently learn, they should be trained and develop their autonomy.</p> <p>Kormos and Csizér ([<reflink idref="bib21" id="ref61">21</reflink>]) examined the mediation effect of self‐regulation on autonomy in computer‐assisted learning conditions and found that time‐management predicted learners' perception of autonomous use of learning resources, but a strong motivation was a prerequisite for the adoption of the strategy. Τhe metacognitive regulation of cognition was also positively related to students' perceptions of autonomy support (Schuitema, Peetsma, & van der Veen, [<reflink idref="bib46" id="ref62">46</reflink>]; Sierens, Vansteenkiste, Goossens, Soenens, & Dochy, [<reflink idref="bib47" id="ref63">47</reflink>]), and learners' deep‐level learning strategies and effort‐regulation during the learning process were also positively related with perceived autonomy support (Vansteenkiste <emph>et al.</emph>, [<reflink idref="bib55" id="ref64">55</reflink>]). The results of another study shown that effort‐regulation was the most important SRL strategy recommended by successful MOOC learners (Kizilcec, Pérez‐Sanagustín, & Maldonado, [<reflink idref="bib20" id="ref65">20</reflink>]). Furthermore, effective help‐seeking has been viewed as an important strategy that enables students to maintain engagement and leads to long‐term mastery and autonomous learning (Puustinen, [<reflink idref="bib40" id="ref66">40</reflink>]; Ryan, Hicks, & Midgley, [<reflink idref="bib44" id="ref67">44</reflink>]). However, it was found that students in online learning conditions perceived desire for autonomy as one of the main reasons for avoiding seeking help (Kizilcec <emph>et al.</emph>, [<reflink idref="bib20" id="ref68">20</reflink>]).</p> <p>There is a lack of empirical knowledge on how SRL strategies are related to learners' exercise of autonomous control. The present study aims to fill this gap by explaining the variance in autonomous interactions based on SRL strategies, using learning analytics. This knowledge is expected to improve our understanding of the self‐regulation mechanisms that can build up autonomy, as well as to allow us to adjust the design of the learning environments. Therefore, the research question (RQ) this study aims to address is defined as follows:</p> <p> <bold>RQ</bold>: What is the effect of self‐regulated learning strategies on learners' autonomous interactions (measured as utilized learning analytics)?</p> <hd id="AN0139230530-6">The research model and hypotheses</hd> <p>This study is contextualized in online self‐assessment conditions. Self‐assessment promotes the development of learners' capacity to self‐regulate their behavior and to preserve their autonomy (McMillan & Hearn, [<reflink idref="bib28" id="ref69">28</reflink>]). A recent contextualization of SRL that mostly apply in online learning environments includes six prevalent strategies, ie, goal‐setting, time‐management/study‐environment, help‐seeking, task‐strategies (eg, effort regulation and rehearsal), peer‐learning and self‐evaluation (Barnard, Lan, To, Paton, & Laic, [<reflink idref="bib3" id="ref70">3</reflink>]). In this study, we formulate hypotheses about the four (out of the six) self‐regulation strategies that have been associate with autonomous learning in literature. In the present study, peer‐learning and self‐evaluation do not apply because the online learning environment is self‐assessment oriented (peer‐learning does not hold, and self‐evaluation is inherent by definition).</p> <p>As such, in order to address the research question, the research hypotheses on the relationships between the considered factors are outlined as follows.</p> <p>Based on the results from Vansteenkiste <emph>et al. </emph>([<reflink idref="bib55" id="ref71">55</reflink>]) and Kizilcec <emph>et al. </emph>([<reflink idref="bib20" id="ref72">20</reflink>]) we believe that the sense of acting freely and making independent choices can be seen as an opportunity for students who properly regulate their efforts according to the requirements of the tasks to exhibit higher autonomous control, as well. Thus:</p> <hd id="AN0139230530-7">H</hd> <p>Effort‐regulation will have a positive effect on Autonomous Control.</p> <p>Goal‐setting is a skill associated with the learners' self‐set learning expectations, whereas self‐set goals produce higher goal commitment (Zimmerman, [<reflink idref="bib59" id="ref73">59</reflink>]). We believe that the learners who are aware of their goals and have high learning and achievement expectations, will take advantage of autonomous control and will select tasks that will facilitate their goals. Therefore:</p> <hd id="AN0139230530-8">H</hd> <p>Goal‐setting will have a positive effect on Autonomous Control.</p> <p>Based on previous arguments (Nelson‐Le Gall, [<reflink idref="bib30" id="ref74">30</reflink>]; Newman, [<reflink idref="bib31" id="ref75">31</reflink>]; Puustinen, [<reflink idref="bib40" id="ref76">40</reflink>]) that seeking understanding‐aided help can lead to greater autonomy, we believe that learners who have strong help‐seeking skills will feel free to ask for multiple levels of hints during dealing with learning tasks. Therefore:</p> <hd id="AN0139230530-9">H</hd> <p>Help‐seeking will have a positive effect on Autonomous Control.</p> <p>Time‐management is a meta‐skill aiming at efficiently allocating time on each of the tasks, within a limited amount of time (Michinov, Brunot, Bohec, Juhel, & Delaval, [<reflink idref="bib29" id="ref77">29</reflink>]). In line with Kormos and Csizér ([<reflink idref="bib21" id="ref78">21</reflink>]), students who believe that have good time‐management skills are expected to allocate time on tasks they have chosen and fine‐tune their time‐management practices. Thus:</p> <hd id="AN0139230530-10">H</hd> <p>Time‐management will have a positive effect on Autonomous Control.</p> <p>Figure illustrates the causal relationships among the considered factors.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01nov19/bjet12747-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet12747-fig-0001.jpg" title="Overall research model and factor relationships with hypotheses" /> </p> <p></p> <hd id="AN0139230530-12">Methods</hd> <p></p> <hd id="AN0139230530-13">Participants and system</hd> <p>Overall, one hundred and thirteen (<reflink idref="bib113" id="ref79">113</reflink>) undergraduate students (51 females [45.1%] and 62 males [54.9%], aged 19–26 years‐old [<emph>M</emph> = 20.74, <emph>SD</emph> = 1.755, <emph>N</emph> = 113]) at a European University were enrolled in an online self‐assessment procedure for the Management Information Systems I course (related to Information Systems, databases and Business Intelligence), at the University lab, for 60 minutes. The participation to the procedure was optional. The self‐assessment tests were offered to facilitate the students' self‐preparation before the final exams, to help them track their progress and align with their learning goals and the scores on these tests had no participation to the final grade (ie, no rewards as external motivation).</p> <hd id="AN0139230530-14">Procedure</hd> <p></p> <hd id="AN0139230530-15">Calibration of the tasks</hd> <p>For the needs of the self‐assessment, 150 multiple‐choice questions (tasks) in total were calibrated before being available to the students. The purpose of the calibration was to allocate each task to one category of difficulty and discrimination ability. The discrimination ability of a task corresponds to the probability of students in a <emph>given mastery class </emph>responding <emph>correctly</emph> to each task. For the tasks' discrimination ability configuration, three mastery classes of students were used (ie, Class A: final grade ≥ 7, Class B: final grade ≥ 4 and Class C: final grade < 4) and the respective probabilities were computed, using the results from prior self‐assessment tests (involving students who have already been classified).</p> <p>Furthermore, two experts agreed on the tasks' difficulty (easy, medium and hard).</p> <hd id="AN0139230530-16">The self‐assessment procedure</hd> <p>For the self‐assessment, the students had to complete up to 12 tasks within the 60 minutes. The rationale behind this restriction is that we wanted to keep the students in line with the four self‐regulation strategies explored in the study: to freely select tasks according to their goal‐setting, to regulate their effort according to the difficulty of the tasks and the limited available time, to manage their time‐allocation per task within limited time‐settings, and to question help‐seeking by downgrading task difficulty (in a "stepping‐back" manner, lowering performance aspiration [Karabenick & Knapp, [<reflink idref="bib19" id="ref80">19</reflink>]]). Each task had four possible answers, but only one was correct.</p> <p>The first task was randomly assigned to the students. The students had full‐autonomy to select the next self‐assessment task according to the desired level of difficulty of that task. As shown in Figure , the students could ask either for a task of the same difficulty with the current one, or for an easier or harder task, or for a random task (delivered to them according to the discrimination ability of the task and the students' currently diagnosed mastery level—the estimation of the students' mastery class is updated every time they submit an answer). The students could skip a task, and ask for a new one, but they could not revise previous (already answered or skipped) tasks. The self‐assessment was finalized either when the 12 tasks were completed (80.1% of the participants), or when a maximum possible score was achieved (ie, 10) (11.6% of the participants), or when the available time of 60 minutes was exceeded (8.3% of the participants). Figure synopsizes this procedure.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01nov19/bjet12747-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet12747-fig-0002.jpg" title="Synopsis of the self‐assessment procedure" /> </p> <p></p> <p>For the score computation, only the correct answers were considered up‐to‐that moment, without penalizing the incorrect answers. Each task's participation on the score was according to its difficulty, varying from 0.5 points (easy) to 1 point (medium) to 1.5 points (hard). The students knew that they could not achieve a maximum score (ie, 10) by selecting only easy tasks, and that if they achieved the maximum score, the quiz would finalize.</p> <p>The online self‐assessment system employed in the study is illustrated in Figure (Papamitsiou & Economides, [<reflink idref="bib33" id="ref81">33</reflink>]).</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01nov19/bjet12747-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet12747-fig-0003.jpg" title="The online self‐assessment environment" /> </p> <p></p> <p>Before taking the self‐assessment, each participant had to answer to a pretest questionnaire that measures the SRL strategies explored in this study, ie, their perceptions of effort‐regulation, goal‐setting, help‐seeking and time‐management. All participants signed an informed consent form prior to their participation. The informed consent explained to them the procedure and was giving the right to researchers to use the data collected for research purposes. Students were aware that their interactions had been tracked and anonymized prior to being analyzed, and that the collected data would be stored for 3 years.</p> <hd id="AN0139230530-19">Data collection</hd> <p></p> <hd id="AN0139230530-20">Measures</hd> <p>Data were collected with the online self‐assessment environment illustrated in Figure , according to the process described in previous section. Measures commonly used in the field of learning analytics (eg, response‐times and frequencies) (Maldonado‐Mahauad <emph>et al.</emph>, [<reflink idref="bib26" id="ref82">26</reflink>]; Papamitsiou & Economides, [<reflink idref="bib34" id="ref83">34</reflink>]; Papamitsiou, Economides, Pappas, & Giannakos, [<reflink idref="bib35" id="ref84">35</reflink>]), indicative of the learners' autonomous control (AC) interactions, were computed from the logged clickstreams. Table illustrates the measures captured and coded, and their descriptive statistics.</p> <p>Measurements used in the study</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Variable</th><th align="center">Mean (Std. Dev.)</th><th align="center">Name</th><th align="center">Description</th></tr></thead><tbody><tr><td align="left">FEAS</td><td align="char" char=".">4.887 (1.549)</td><td align="left">Frequency of choosing easier</td><td align="left">How many times the student asks for easier questions (compared to the current one)</td></tr><tr><td align="left">FHAR</td><td align="char" char=".">3.494 (1.631)</td><td align="left">Frequency of choosing harder</td><td align="left">How many times the student asks for harder questions (compared to the current one)</td></tr><tr><td align="left">FSAM</td><td align="char" char=".">3.173 (1.281)</td><td align="left">Frequency of choosing same</td><td align="left">How many times the student asks for question of the same difficulty with the current one</td></tr><tr><td align="left">FRAN</td><td align="char" char=".">1.708 (1.005)</td><td align="left">Frequency of choosing random</td><td align="left">How many times the student asks for random questions (automatically assigned)</td></tr><tr><td align="left">FESC</td><td align="char" char=".">1.223 (1.012)</td><td align="left">Frequency of skipping</td><td align="left">How many times the student ignores a question</td></tr><tr><td align="left">FTEH</td><td align="char" char=".">2.133 (1.127)</td><td align="left">Frequency of transition from easier to harder</td><td align="left">How many times the student asks for a harder question after an easier one (ie, from easy to medium or hard, and from medium to hard)</td></tr><tr><td align="left">FTHE</td><td align="char" char=".">2.843 (1.224)</td><td align="left">Frequency of transition from harder to easier</td><td align="left">How many times the student asks for an easier question after a harder one (ie, from hard to medium or easy and from medium to easy)</td></tr><tr><td align="left">TTDM</td><td align="char" char=".">62.236 (32.072)</td><td align="left">Time‐spent on decision making</td><td align="left">The time interval between answering a question and choosing the next one</td></tr><tr><td align="left">SST</td><td align="char" char=".">1386.04 (437.694)</td><td align="left">Study session time</td><td align="left">The total study session time for each student</td></tr></tbody></table> </ephtml> </p> <p>The AC variable was modeled using Partial Least Squares (PLS) for dimensionality reduction. In the initial model, for AC the factor loadings for FESC, FTEH, FTHE and SST were lower than 0.7 and as such they were removed from the model. The consistency reliability of the factor that measures autonomy was confirmed (<emph>a</emph> = 0.791).</p> <hd id="AN0139230530-21">Instruments</hd> <p>In order to develop the instrument for measuring the four SRL strategies, we adapted some items of the constructs from previously validated instruments. For students' effort‐regulation (ER) we adopted three items from the Motivated Strategies for Learning Questionnaire (MSLQ; Pintrich, Smith, Garcia, & Mckeachie, [<reflink idref="bib39" id="ref85">39</reflink>]). For goal‐setting, we used the goal‐expectancy construct, and configured three items from Terzis and Economides ([<reflink idref="bib52" id="ref86">52</reflink>]). Goal‐expectancy (GE) is a measure of goal‐setting particularized on assessment procedures. For help‐seeking (HS), three items were adopted from the Online Self‐Regulated Learning Questionnaire (OSLQ; Barnard <emph>et al.</emph>, [<reflink idref="bib3" id="ref87">3</reflink>]). Finally, for time‐management (TM) we configured three items from the OSLQ, as well. The items from each instrument were selected and modified for the context of the self‐assessment (for all constructs, Cronbach's was above 0.7, ensuring internal consistency). The questionnaire was first developed in English and then translated into the students' native language, by certified translators to ensure linguistic equivalence. All items were measured in a 7‐point Likert‐like scale (1 = strongly disagree to 7 = strongly agree, Appendix Table A1).</p> <hd id="AN0139230530-22">Data analysis</hd> <p></p> <hd id="AN0139230530-23">Structural and measurement model</hd> <p>For addressing the research question, the construction of a path diagram that contains the structural and measurement model was conducted with the Partial least‐squares Structural Equation Modeling (PLS‐SEM) technique (Chin, [<reflink idref="bib8" id="ref88">8</reflink>]; Tenenhaus, Vinzi, Chatelin, & Lauro, [<reflink idref="bib51" id="ref89">51</reflink>]). PLS‐SEM is an exploratory technique that allows for estimating and directly testing theoretically proposed chains of cause and effect between the latent variables (Sarstedt, Ringle, & Hair, [<reflink idref="bib45" id="ref90">45</reflink>]). PLS is a non‐parametric, distribution‐free method and was selected to reduce the predictors to a smaller set of uncorrelated components and perform least‐squares regression on them. Our sample of 113 participants exceeds the recommended value of 50, ie, (a) 10 times larger than the number of items for the most complex construct (AC with five items), and (b) 10 times the largest number of independent variables impact a dependent variable (ER, GE, HS, TM to AC).</p> <hd id="AN0139230530-24">Measures and evaluation criteria</hd> <p>The structural model evaluates the relationship between exogenous and endogenous latent variables by examining the variance measured (<emph>R</emph><sups>2</sups>). <emph>R</emph><sups>2</sups> values of 0.67, 0.33 and 0.19 are substantial, moderate and weak respectively (Chin, [<reflink idref="bib8" id="ref91">8</reflink>]). The quality of path model can be evaluated by the Stone‐Geisser's <emph>Q</emph><sups>2</sups> value (Geisser, [<reflink idref="bib13" id="ref92">13</reflink>]; Stone, [<reflink idref="bib49" id="ref93">49</reflink>]), an evaluation criterion for the cross‐validated predictive relevance of the PLS path model. The <emph>Q</emph><sups>2</sups> statistic measures the predictive relevance of the model by reproducing the observed values by the model itself. A <emph>Q</emph><sups>2</sups> greater than 0 means the model has predictive relevance; <emph>Q</emph><sups>2</sups> statistic less than 0 mean that the model lacks predictive relevance. Finally, a bootstrap procedure evaluates the significance of the path coefficients (<emph>β</emph> value) and total effects, by calculating <emph>t</emph>‐values. For the measurement and the structural model we used SmartPLS 3.2.</p> <hd id="AN0139230530-25">Results</hd> <p></p> <hd id="AN0139230530-26">Convergent validity—discriminant validity</hd> <p>The results support the measurement model. All criteria for convergent validity are met: the items' factor loadings on the corresponded constructs are higher than 0.7 (Chin, [<reflink idref="bib8" id="ref94">8</reflink>]), Cronbach's a and composite reliability of all constructs are higher than 0.7 and confirm reliability of the measurement model, and Average Variance Extracted (AVE) is higher than 0.5, exceeding the variance due to measurement error for that construct. Table displays these results.</p> <p>Results for the latent constructs of the measurement model</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Construct items</th><th align="center">Factor loadings (>0.7)<xref ref-type="fn" rid="tfn2" /></th><th align="center">Cronbach's a (>0.7)<xref ref-type="fn" rid="tfn2" /></th><th align="center">Composite reliability (>0.7)<xref ref-type="fn" rid="tfn2" /></th><th align="center">Average variance extracted (>0.5)<xref ref-type="fn" rid="tfn2" /></th></tr></thead><tbody><tr><td align="left">ER</td><td align="char" char="." /><td align="char" char=".">0.777</td><td align="char" char=".">0.857</td><td align="char" char=".">0.602</td></tr><tr><td align="left">ER1</td><td align="char" char=".">0.818</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">ER2</td><td align="char" char=".">0.853</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">ER3</td><td align="char" char=".">0.823</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">GE</td><td align="char" char="." /><td align="char" char=".">0.871</td><td align="char" char=".">0.920</td><td align="char" char=".">0.794</td></tr><tr><td align="left">GE1</td><td align="char" char=".">0.885</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">GE2</td><td align="char" char=".">0.893</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">GE3</td><td align="char" char=".">0.895</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">HS</td><td align="char" char="." /><td align="char" char=".">0.797</td><td align="char" char=".">0.871</td><td align="char" char=".">0.693</td></tr><tr><td align="left">HS1</td><td align="char" char=".">0.906</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">HS2</td><td align="char" char=".">0.857</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">HS3</td><td align="char" char=".">0.725</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">TM</td><td align="char" char="." /><td align="char" char=".">0.853</td><td align="char" char=".">0.911</td><td align="char" char=".">0.773</td></tr><tr><td align="left">TM1</td><td align="char" char=".">0.908</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">TM2</td><td align="char" char=".">0.851</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">TM3</td><td align="char" char=".">0.877</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">AC</td><td align="char" char="." /><td align="char" char=".">0.791</td><td align="char" char=".">0.864</td><td align="char" char=".">0.625</td></tr><tr><td align="left">FEAS</td><td align="char" char=".">0.803</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">FHAR</td><td align="char" char=".">0.871</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">FSAM</td><td align="char" char=".">0.702</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">FRAN</td><td align="char" char=".">0.709</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">TTDM</td><td align="char" char=".">0.716</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr></tbody></table> </ephtml> </p> <p>1 ER: Effort‐regulation, GE: Goal‐expectancy, HS: Help‐seeking, TM: Time‐management, AC: Autonomous Control, FEAS: Frequency of choosing easier, FHAR: Frequency of choosing harder, FSAM: Frequency of choosing same, FRAN: Frequency of choosing random, TTDM: Time‐spent on decision making.</p> <p>2 Indicates an acceptable level of reliability and validity.</p> <p>Discriminant validity is also confirmed since the AVE of each construct is higher than the construct's highest squared correlation with any other construct (Fornell & Larcker, [<reflink idref="bib12" id="ref95">12</reflink>]). Table presents the variables' correlation matrix; the diagonal elements are the square root of the AVE of a construct.</p> <p>Measurement model (discriminant validity)</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left" /><th align="center">Mean (Std. Dev.)</th><th align="center">1</th><th align="center">2</th><th align="center">3</th><th align="center">4</th><th align="center">5</th></tr></thead><tbody><tr><td align="left">1. Effort‐regulation</td><td align="char" char=".">4.553 (1.212)</td><td align="char" char=".">0.831</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">2. Goal‐expectancy</td><td align="char" char=".">4.539 (1.342)</td><td align="char" char=".">0.604</td><td align="char" char=".">0.891</td><td align="char" char="." /><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">3. Help‐seeking</td><td align="char" char=".">5.454 (1.307)</td><td align="char" char=".">0.334</td><td align="char" char=".">0.595</td><td align="char" char=".">0.833</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">4. Time‐management</td><td align="char" char=".">4.684 (1.296)</td><td align="char" char=".">0.616</td><td align="char" char=".">0.708</td><td align="char" char=".">0.480</td><td align="char" char=".">0.879</td><td align="char" char="." /></tr><tr><td align="left">5. Autonomous control</td><td align="char" char=".">2.637 (1.012)</td><td align="char" char=".">0.422</td><td align="char" char=".">0.501</td><td align="char" char=".">0.187</td><td align="char" char=".">0.488</td><td align="char" char=".">0.776</td></tr></tbody></table> </ephtml> </p> <hd id="AN0139230530-27">Testing hypotheses</hd> <p>A bootstrap procedure with 3000 resamples was used to test the statistical significance (<emph>t</emph>‐value) of the path coefficients (<emph>β </emph>value) in the model. Table summarizes the results for the hypotheses testing.</p> <p>Hypothesis testing results</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Hypothesis</th><th align="center">Path</th><th align="center"><italic>β</italic></th><th align="center"><italic>t</italic></th><th align="center"><italic>P</italic></th><th align="center">Result</th></tr></thead><tbody><tr><td align="left">H1</td><td align="left">Effort‐regulation → Autonomous control</td><td align="char" char=".">0.105</td><td align="char" char=".">1.076</td><td align="char" char=".">0.283</td><td align="center">Not support</td></tr><tr><td align="left">H2</td><td align="left">Goal‐expectancy → Autonomous control</td><td align="char" char=".">0.374</td><td align="char" char=".">3.388<xref ref-type="fn" rid="tfn3" /></td><td align="char" char=".">0.001</td><td align="center">Support</td></tr><tr><td align="left">H3</td><td align="left">Help‐seeking → Autonomous control</td><td align="char" char=".">−0.190</td><td align="char" char=".">2.154<xref ref-type="fn" rid="tfn3" /></td><td align="char" char=".">0.032</td><td align="center">Support opposite</td></tr><tr><td align="left">H4</td><td align="left">Time‐management → Autonomous control</td><td align="char" char=".">0.250</td><td align="char" char=".">2.244<xref ref-type="fn" rid="tfn3" /></td><td align="char" char=".">0.025</td><td align="center">Support</td></tr></tbody></table> </ephtml> </p> <p>3 * <emph>p</emph> < 0.05.</p> <p>As seen from this table, two of the initial hypotheses are supported, one is not supported and for one hypothesis, its negation is supported. These results are discussed in next section.</p> <hd id="AN0139230530-28">Overall model fit</hd> <p>According to these results, the suggested model explains almost the 33% of the variance in autonomous control, which is statistically moderate. The cross‐validated predictive relevance of the model was also confirmed (<emph>Q</emph><sups>2</sups> = 0.261). Table synopsizes the total effects of the selected factors, as well as the variance (<emph>R</emph><sups>2</sups>) and cross‐validated predictive relevance (<emph>Q</emph><sups>2</sups>) explained by the proposed model.</p> <p>R 2 , Q 2 and total effects</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Endogenous</th><th align="center"><italic>R</italic><sup>2</sup></th><th align="center"><italic>Q</italic><sup>2</sup></th><th align="center">Exogenous</th><th align="center">Total effect</th><th align="center"><italic>t</italic></th></tr></thead><tbody><tr><td align="left">Autonomous Control</td><td align="char" char=".">0.332</td><td align="char" char=".">0.261</td><td align="left">Effort‐regulation</td><td align="char" char=".">0.105</td><td align="char" char=".">1.076</td></tr><tr><td align="left" /><td align="char" char="." /><td align="char" char="." /><td align="left">Goal‐expectancy</td><td align="char" char=".">0.374</td><td align="char" char=".">3.388<xref ref-type="fn" rid="tfn4" /></td></tr><tr><td align="left" /><td align="char" char="." /><td align="char" char="." /><td align="left">Help‐seeking</td><td align="char" char=".">−0.190</td><td align="char" char=".">2.154<xref ref-type="fn" rid="tfn4" /></td></tr><tr><td align="left" /><td align="char" char="." /><td align="char" char="." /><td align="left">Time‐management</td><td align="char" char=".">0.250</td><td align="char" char=".">2.244<xref ref-type="fn" rid="tfn4" /></td></tr></tbody></table> </ephtml> </p> <p>4 * <emph>p</emph> < 0.05.</p> <p>The measurement results are summarized in Figure. This figure illustrates the path coefficients for the initial hypotheses of the research model.</p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/58I/01nov19/bjet12747-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="bjet12747-fig-0004.jpg" title="Path coefficients of the research model, overall variance explained (R2) for test score and cross‐validated predictive relevance (Q2)" /> </p> <p></p> <hd id="AN0139230530-30">Discussion</hd> <p></p> <hd id="AN0139230530-31">Findings and interpretations</hd> <p>Contemporary online learning environments provide learners more opportunities to freely choose what, where and how to learn, eliminating the restriction of place, time and physical materials (Broadbent & Poon, [<reflink idref="bib5" id="ref96">5</reflink>]; Xu & Jaggars, [<reflink idref="bib58" id="ref97">58</reflink>]). However, learners do not intuitively know how to build their autonomous learning capacity and make efficient learning choices. Autonomy‐supportive teaching and autonomy‐supportive design of learning environments have been proposed to increase autonomous motivation, engagement and self‐regulation (eg, Andrade, [<reflink idref="bib1" id="ref98">1</reflink>]; Jossberger <emph>et al.</emph>, [<reflink idref="bib18" id="ref99">18</reflink>]; Reeve, [<reflink idref="bib41" id="ref100">41</reflink>]; Sun & Rueda, [<reflink idref="bib50" id="ref101">50</reflink>]; Vansteenkiste <emph>et al.</emph>, [<reflink idref="bib54" id="ref102">54</reflink>]). The question is <emph>how</emph> SRL strategies influence the learners' autonomous interactions, within inherently autonomous online learning environments.</p> <p>Previous studies relied on learners' perceptions of autonomy. The present study targets at providing empirical evidence on how SLR strategies predict and justify the learners' self‐enforced choices, using learning analytics.</p> <p>In particular, in the demonstrated approach, autonomy was modeled in terms of frequencies of interaction types and time‐spent on decision making, both commonly used in the learning analytics research practise (Maldonado‐Mahauad <emph>et al.</emph>, [<reflink idref="bib26" id="ref103">26</reflink>]; Papamitsiou & Economides, [<reflink idref="bib34" id="ref104">34</reflink>]; Papamitsiou <emph>et al.</emph>, [<reflink idref="bib35" id="ref105">35</reflink>]). Next, a set of specific SRL strategies that have been identified in previous research as critical in online learning contexts (Broadbent & Poon, [<reflink idref="bib5" id="ref106">5</reflink>]; Kizilcec <emph>et al.</emph>, [<reflink idref="bib20" id="ref107">20</reflink>]; Kormos & Csizér, [<reflink idref="bib21" id="ref108">21</reflink>]; Sierens <emph>et al.</emph>, [<reflink idref="bib47" id="ref109">47</reflink>]), and that could be associated to or infer autonomous behavior, were explored through a structural and measurement model. These factors were measured with configured versions of previously validated instruments, and their consistency reliability was also confirmed (Table).</p> <p>The overall prediction accuracy of the model proposed in this study was 33.2%, which is moderate, and the cross‐validated predictive relevance was confirmed (<emph>Q</emph><sups>2</sups> = 26.1%) (Table). This finding contributes to understanding the diversity of the two concepts and provides empirical evidence that exercising self‐regulation strategies <emph>is not enough</emph> for experiencing autonomous control. Apparently, additional factors should be considered as well. For example, the degree of autonomy each learner achieves depends on the characteristics of the learner. Olesen <emph>et al. </emph>([<reflink idref="bib32" id="ref110">32</reflink>]) argued that this relationship is catalyzed by personality traits.</p> <p>Moreover, the analysis revealed a strong direct positive effect of goal‐setting and time‐management on autonomous control (<emph>β = </emph>0.374,<emph> t = </emph>3.388,<emph> p = </emph>0.001;<emph> β = </emph>0.250, <emph>t = </emph>2.244,<emph> p = </emph>0.025 for GE and TM respectively), confirming hypotheses H2 and H4, in line with and further confirming previous results (Kormos & Csizér, [<reflink idref="bib21" id="ref111">21</reflink>]). Practically, it extends previous findings by adding empirical evidence, and it implies two posits: (a) learners who are aware of their goals and have high learning and achievement expectations, will try to select tasks that will facilitate their goals, and (b) learners who believe that they have good time‐management skills, will take the opportunity for autonomous control in order to adjust and fine‐tune these skills.</p> <p>In addition, a moderate positive effect of effort‐regulation on autonomy (<emph>β = </emph>0.105, <emph>t = </emph>1.076,<emph> p = </emph>0.283) was detected, neither supporting nor rejecting hypothesis H1. In previous research (Vansteenkiste <emph>et al.</emph>, [<reflink idref="bib55" id="ref112">55</reflink>]) effort‐regulation was found to be a strong determinant of perceived autonomy. The slight divergence of our finding might be due to <emph>how autonomy was measured</emph>, highlighting the difference between intentions and real behaviors (Gollwitzer <emph>et al.</emph>, [<reflink idref="bib14" id="ref113">14</reflink>]). Based on our results, the role of this factor is not clear: although it seems that learners' perceptions of persistence in their engagement with the learning tasks might influence their self‐enforced choice of tasks, however, in practise, effort expenditure on the selected tasks would possibly clarify more this relationship.</p> <p>The most intriguing finding of this study concerns the strong direct negative effect of the help‐seeking factor on autonomous interactions (<emph>β = −</emph>0.190<emph>, t = </emph>2.154,<emph> p = </emph>0.032), resulting in supporting the opposite of hypothesis H3 (Table). Due to the highly self‐initiating nature of help‐seeking behavior, and in line with Nelson‐Le Gall's ([<reflink idref="bib30" id="ref114">30</reflink>]) claims that help‐seeking can promote autonomy, we assumed that help‐seeking would positively influence autonomous control. Online learning environments provide increased opportunities for free help‐seeking and they can preserve learners' anonymity. However, this hypothesis was not confirmed, yet, the opposite result was discovered. This finding is in line with the findings in Kizilcec <emph>et al. </emph>([<reflink idref="bib20" id="ref115">20</reflink>]), that help‐seeking contradicts autonomous control. A possible explanation of this result is that learners might perceive help‐seeking as a threat to their autonomy (Huet, Escribe, Dupeyrat, & Sakdavong, [<reflink idref="bib17" id="ref116">17</reflink>]). This finding requires attention and should be further explored from different perspectives, including actual measurements of help‐seeking behavior in environments that facilitate autonomous learning conditions.</p> <hd id="AN0139230530-32">Conclusions</hd> <p>In the introductory example, the learner is intrinsically directed to study and learn about a topic, and she freely yet responsibly makes self‐enforced decisions and choices for her learning, guided by her own volition. At a macro‐level, it is the learner she who determines <emph>why, what, when, how</emph> and <emph>how much</emph> to learn, and has the overall control of the learning process, and it is also the learner she who <emph>initiates</emph> the learning cycle (autonomy). At a meso‐level, the learner regulates her behavior and actions and makes "contextualized" decisions: she still has the control and responsibility for her choices, but <emph>the options derive from the context</emph> and not from the inner self. At this level, the learner regulates her behavior and decides the degree up‐to which she follows the subsequent steps available to her according to the specific guidelines and/or requirements of the learning context and the learning environment (self‐regulation). The latest ones are defined at a micro‐level by the tutor/designer of the course/environment. In a sense, autonomy is broader than self‐regulation, and the results of the present study provide strong indications towards supporting this claim, but further research is required on this direction.</p> <hd id="AN0139230530-33">Implications for research and practice</hd> <p>The findings offer implications for research and practice. Firstly, exploring additional factors (eg, personality) as moderators of this relationship is required. Furthermore, understanding how learners' self‐regulation contributes to and reflects their autonomy can provide insight on how to plan the learners' SRL support in online learning environments (research). As the next step, practitioners shall be able to integrate specific features into online learning environment, in order to train learners on effectively using the SRL strategies, accordingly (practice). For example, supporting learners' effort‐regulation (eg, with learning analytics visualizations) might help the learners to persist on their on‐task engagement and guide them to choose tasks that better correspond to their learning goals.</p> <p>Moreover, attention is required regarding the impact of help‐seeking on self‐directed learning, and the relationship between both self‐initiated behaviors should be further explored (research). For example, providing a help‐seeking functionality to the learners, measuring their interactions with this facility, and exploring the next autonomous choices of learning tasks, would provide additional empirical evidence on the effect of help‐seeking on autonomy. Clarifying this relationship shall next open new directions towards making decisions on how to efficiently guide the learners to feel free to seek help (practice).</p> <hd id="AN0139230530-34">Limitations and future work</hd> <p>Of course, there are limitations as well. First, the sample size (<emph>N</emph> = 113) of the present study is relatively small, yet sufficient for the analysis methods employed. Larger samples should be explored to further validate the demonstrated findings. Second, two SRL strategies, ie, peer‐learning and self‐evaluation did not apply on the context of this study, and need to be considered for analysis, as well. Last, the 60 minutes of the present study is a very limited time interval. The duration of experimentation with respective procedures should increase. This is within our future work plans.</p> <hd id="AN0139230530-35">Statements on open data, ethics and conflict of interest</hd> <p>The data can be obtained by request, by contacting the corresponding author.</p> <p>Participation was voluntarily and all the data collected anonymously. Appropriate permissions and ethical approval for the participation requested and approved.</p> <p>There is no potential conflict of interest in this study.</p> <hd id="AN0139230530-36">Acknowledgements</hd> <p>This research did not receive any specific grant from funding agencies in the public, commercial or not‐for‐profit sectors.</p> <p>Appendix</p> <p>Constructs and items from the questionnaires</p> <p> <ephtml> <table><thead valign="top"><tr><th align="left">Construct</th><th align="center">Items</th><th align="center">Description</th></tr></thead><tbody><tr><td align="left">Effort‐Regulation (ER)—MSLQ; Pintrich <italic>et al. </italic>(<xref ref-type="bibr" rid="bibr39">39</xref>)</td><td align="left">ER1</td><td align="left">I work hard to do well in this class even if I don't like what we are doing</td></tr><tr><td align="left">ER2</td><td align="left">Even when course materials are dull and uninteresting, I manage to keep working until I finish</td></tr><tr><td align="left">ER3</td><td align="left">I often feel so lazy or bored when I study for this class that I quit before I finish what I planned to do</td></tr><tr><td align="left">Goal Expectancy (GE) – CBAAM; Terzis and Economides (<xref ref-type="bibr" rid="bibr52">52</xref>)</td><td align="left">GE1</td><td align="left">Courses' preparation was sufficient for the test</td></tr><tr><td align="left">GE2</td><td align="left">My personal preparation for the test was sufficient</td></tr><tr><td align="left">GE3</td><td align="left">My performance expectations for the test</td></tr><tr><td align="left">Help‐Seeking (HS)—OSLQ; Barnard <italic>et al. </italic>(<xref ref-type="bibr" rid="bibr3">3</xref>)</td><td align="left">HS1</td><td align="left"><p>I am persistent in getting help from the instructor through e‐mail</p><p>I find someone who is knowledgeable in course content so that I can consult with him or her when I need help</p></td></tr><tr><td align="left">HS2</td></tr><tr><td align="left">HS3</td><td align="left">I share my problems with my classmates online so we know what we are struggling with and how to solve our problems</td></tr><tr><td align="left">Time‐Management (TM)—OSLQ; Barnard <italic>et al. </italic>(<xref ref-type="bibr" rid="bibr3">3</xref>)</td><td align="left">TM1</td><td align="left">I allocate extra studying time for my online courses because I know it is time‐demanding</td></tr><tr><td align="left">TM2</td><td align="left">I try to schedule the same time every day or every week to study for my online courses, and I observe the schedule</td></tr><tr><td align="left">TM3</td><td align="left">Although we don't have to attend daily classes, I still try to distribute my studying time evenly across days</td></tr></tbody></table> </ephtml> </p> <ref id="AN0139230530-37"> <title> References </title> <blist> <bibl id="bib1" idref="ref6" type="bt">1</bibl> <bibtext> Andrade, M. 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Economides</p> <p>Reported by Author; Author</p> <p></p> <p>Zacharoula Papamitsiou is a Computer Science and Informatics teacher in secondary education, and a junior researcher at SMILE (Smart and Mobile Interactive Learning Environments) Lab. Currently she is a Ph.D. candidate in Information Systems at the University of Macedonia. Her domain of expertise is on adapting and personalizing the learning services and on supporting learners' decision making using Learning Analytics.</p> <p>Anastasios A. Economides is Full Professor on Computer Networks & Telematics Applications and Director of the SMILE (Smart & Mobile Interactive Learning Environments) laboratory at the University of Macedonia, Thessaloniki, Greece. His research interests include personalized and collaborative learning & assessment, user experience and acceptance of smart systems and services and networking techno‐economics.</p> </aug> <nolink nlid="nl1" bibid="bib10" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib25" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib36" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib41" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib57" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib18" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib24" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib37" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib43" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib27" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib60" firstref="ref16"></nolink> <nolink nlid="nl12" bibid="bib15" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib16" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib22" firstref="ref22"></nolink> <nolink nlid="nl15" bibid="bib38" firstref="ref26"></nolink> <nolink nlid="nl16" bibid="bib42" firstref="ref27"></nolink> <nolink nlid="nl17" bibid="bib48" firstref="ref28"></nolink> <nolink nlid="nl18" bibid="bib55" firstref="ref29"></nolink> <nolink nlid="nl19" bibid="bib59" firstref="ref31"></nolink> <nolink nlid="nl20" bibid="bib53" firstref="ref34"></nolink> <nolink nlid="nl21" bibid="bib56" firstref="ref35"></nolink> <nolink nlid="nl22" bibid="bib26" firstref="ref37"></nolink> <nolink nlid="nl23" bibid="bib11" firstref="ref41"></nolink> <nolink nlid="nl24" bibid="bib32" firstref="ref44"></nolink> <nolink nlid="nl25" bibid="bib23" firstref="ref46"></nolink> <nolink nlid="nl26" bibid="bib50" firstref="ref50"></nolink> <nolink nlid="nl27" bibid="bib54" firstref="ref51"></nolink> <nolink nlid="nl28" bibid="bib58" firstref="ref57"></nolink> <nolink nlid="nl29" bibid="bib21" firstref="ref61"></nolink> <nolink nlid="nl30" bibid="bib46" firstref="ref62"></nolink> <nolink nlid="nl31" bibid="bib47" firstref="ref63"></nolink> <nolink nlid="nl32" bibid="bib20" firstref="ref65"></nolink> <nolink nlid="nl33" bibid="bib40" firstref="ref66"></nolink> <nolink nlid="nl34" bibid="bib44" firstref="ref67"></nolink> <nolink nlid="nl35" bibid="bib28" firstref="ref69"></nolink> <nolink nlid="nl36" bibid="bib30" firstref="ref74"></nolink> <nolink nlid="nl37" bibid="bib31" firstref="ref75"></nolink> <nolink nlid="nl38" bibid="bib29" firstref="ref77"></nolink> <nolink nlid="nl39" bibid="bib113" firstref="ref79"></nolink> <nolink nlid="nl40" bibid="bib19" firstref="ref80"></nolink> <nolink nlid="nl41" bibid="bib33" firstref="ref81"></nolink> <nolink nlid="nl42" bibid="bib34" firstref="ref83"></nolink> <nolink nlid="nl43" bibid="bib35" firstref="ref84"></nolink> <nolink nlid="nl44" bibid="bib39" firstref="ref85"></nolink> <nolink nlid="nl45" bibid="bib52" firstref="ref86"></nolink> <nolink nlid="nl46" bibid="bib51" firstref="ref89"></nolink> <nolink nlid="nl47" bibid="bib45" firstref="ref90"></nolink> <nolink nlid="nl48" bibid="bib13" firstref="ref92"></nolink> <nolink nlid="nl49" bibid="bib49" firstref="ref93"></nolink> <nolink nlid="nl50" bibid="bib12" firstref="ref95"></nolink> <nolink nlid="nl51" bibid="bib14" firstref="ref113"></nolink> <nolink nlid="nl52" bibid="bib17" firstref="ref116"></nolink>
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Exploring Autonomous Learning Capacity from a Self-Regulated Learning Perspective Using Learning Analytics
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Papamitsiou%2C+Zacharoula%22">Papamitsiou, Zacharoula</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-0982-3623">0000-0002-0982-3623</externalLink>)<br /><searchLink fieldCode="AR" term="%22Economides%2C+Anastasios+A%2E%22">Economides, Anastasios A.</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22British+Journal+of+Educational+Technology%22"><i>British Journal of Educational Technology</i></searchLink>. Nov 2019 50(6):3138-3155.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley-Blackwell. 350 Main Street, Malden, MA 02148. Tel: 800-835-6770; Tel: 781-388-8598; Fax: 781-388-8232; e-mail: cs-journals@wiley.com; Web site: http://www.wiley.com/WileyCDA
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 18
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2019
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+Study%22">Independent Study</searchLink><br /><searchLink fieldCode="DE" term="%22Personal+Autonomy%22">Personal Autonomy</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Strategies%22">Learning Strategies</searchLink><br /><searchLink fieldCode="DE" term="%22Decision+Making%22">Decision Making</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Goal+Orientation%22">Goal Orientation</searchLink><br /><searchLink fieldCode="DE" term="%22Help+Seeking%22">Help Seeking</searchLink><br /><searchLink fieldCode="DE" term="%22Time+Management%22">Time Management</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/bjet.12747
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 0007-1013
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Practising self-regulated learning (SRL) has been proposed to develop learning autonomy. However, there is lack of empirical evidence on how SRL strategies affect autonomous learning capacity. This study attempts to bridge that gap by utilizing the learners' trace data for measuring the learners' autonomous interactions, and investigates the effects of four SRL strategies on learners' autonomous choices. The goal is to explain how the employed SRL strategies impact autonomous control (in terms of frequencies of self-enforced decisions, as well as time-spent on decision making). The results from an exploratory study with undergraduate learners (N = 113) shown that goal-setting and time-management have strong positive effects on autonomous control, effort-regulation moderately positively affects learners' autonomy, while help-seeking has a strong negative effect. These findings provide empirical evidence and contribute to clarifying the role of each one of the SRL strategies in the development of autonomous learning capacity, from a learning analytics perspective. Limitations and potential implications for research and practice are also discussed.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2019
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1232163
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1232163
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    Identifiers:
      – Type: doi
        Value: 10.1111/bjet.12747
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 18
        StartPage: 3138
    Subjects:
      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Independent Study
        Type: general
      – SubjectFull: Personal Autonomy
        Type: general
      – SubjectFull: Learning Strategies
        Type: general
      – SubjectFull: Decision Making
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Goal Orientation
        Type: general
      – SubjectFull: Help Seeking
        Type: general
      – SubjectFull: Time Management
        Type: general
    Titles:
      – TitleFull: Exploring Autonomous Learning Capacity from a Self-Regulated Learning Perspective Using Learning Analytics
        Type: main
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            NameFull: Papamitsiou, Zacharoula
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            NameFull: Economides, Anastasios A.
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              Y: 2019
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            – TitleFull: British Journal of Educational Technology
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