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Bridging the Digital Divide: The Mediating Role of Learning Engagement between Technology Usage Approaches and Higher Order Thinking Skills in a Technology-Enhanced Inquiry-Based Learning Environment

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Title: Bridging the Digital Divide: The Mediating Role of Learning Engagement between Technology Usage Approaches and Higher Order Thinking Skills in a Technology-Enhanced Inquiry-Based Learning Environment
Language: English
Authors: Kaili Lu, Tongling Ji, Feng Lu, Rustam Shadiev (ORCID 0000-0001-5571-1158)
Source: Educational Technology Research and Development. 2025 73(5):2929-2949.
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: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Learner Engagement, Access to Computers, Disadvantaged, Technology Uses in Education, Thinking Skills, Cognitive Processes, Inquiry, Mediation Theory
DOI: 10.1007/s11423-025-10533-7
ISSN: 1042-1629
1556-6501
Abstract: The integration of technology in the learning landscape has precipitated the need to understand its relationship with students' cognitive processes. However, there is a gap in understanding how learning engagement interacts with two approaches to using technology and how these, in turn, impact higher order thinking skills (HOTS). This study aimed to explore the mediating role of learning engagement between approaches to using technology and HOTS within the technology-enhanced inquiry-based learning (T-IBL) framework. Data were collected from a sample of 160 college students experienced in T-IBL environments. Structural equation modeling was used to analyse the relationship between these key variables. The results showed that students' deep approach to using technologies had direct and significant positive impacts on learning engagement and HOTS. While students' surface approach to using technologies had direct negative influences on learning engagement and HOTS, they were not significant. What is more, learning engagement had direct and significant positive impacts on HOTS. In other words, learning engagement act as a mediator between students' deep approach to using technologies and HOTS, but not between surface approach and HOTS. This research fills an existing gap by elucidating the intricate relationship between technology use, engagement, and cognitive processes in a T-IBL setting. The findings underscore the importance of fostering deeper engagement and mindful technology use to enhance HOTS in learners, offering invaluable insights for educators and curriculum developers in the digital age.
Abstractor: As Provided
Entry Date: 2026
Accession Number: EJ1497657
Database: ERIC
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  Value: <anid>AN0189592951;etr01oct.25;2025Nov28.05:47;v2.2.500</anid> <title id="AN0189592951-1">Bridging the digital divide: the mediating role of learning engagement between technology usage approaches and higher order thinking skills in a technology-enhanced inquiry-based learning environment </title> <p>The integration of technology in the learning landscape has precipitated the need to understand its relationship with students' cognitive processes. However, there is a gap in understanding how learning engagement interacts with two approaches to using technology and how these, in turn, impact higher order thinking skills (HOTS). This study aimed to explore the mediating role of learning engagement between approaches to using technology and HOTS within the technology-enhanced inquiry-based learning (T-IBL) framework. Data were collected from a sample of 160 college students experienced in T-IBL environments. Structural equation modeling was used to analyse the relationship between these key variables. The results showed that students' deep approach to using technologies had direct and significant positive impacts on learning engagement and HOTS. While students' surface approach to using technologies had direct negative influences on learning engagement and HOTS, they were not significant. What is more, learning engagement had direct and significant positive impacts on HOTS. In other words, learning engagement act as a mediator between students' deep approach to using technologies and HOTS, but not between surface approach and HOTS. This research fills an existing gap by elucidating the intricate relationship between technology use, engagement, and cognitive processes in a T-IBL setting. The findings underscore the importance of fostering deeper engagement and mindful technology use to enhance HOTS in learners, offering invaluable insights for educators and curriculum developers in the digital age.</p> <p>Keywords: Mediating effect; Learning engagement; Approaches; Technology; HOTS; Technology-enhanced inquiry-based learning context; Education Curriculum and Pedagogy Specialist Studies In Education</p> <p>Copyright comment Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</p> <hd id="AN0189592951-2">Introduction</hd> <p></p> <hd id="AN0189592951-3">Background</hd> <p>For decades, inquiry-based learning (IBL) has been celebrated as a pivotal student-centered methodology, finding relevance not just in the realms of science and mathematics, but also within arts disciplines (Chou et al., [<reflink idref="bib15" id="ref1">15</reflink>]; Hwang et al., [<reflink idref="bib34" id="ref2">34</reflink>]; Kusmin et al., [<reflink idref="bib47" id="ref3">47</reflink>]; Liu et al., [<reflink idref="bib50" id="ref4">50</reflink>]). Its growing prevalence in higher education (Pow & Li, [<reflink idref="bib65" id="ref5">65</reflink>]) stems from its potency in enhancing students' higher order thinking skills (HOTS) and other vital 21st-century competencies (Chang et al., [<reflink idref="bib9" id="ref6">9</reflink>]). These skills equip college students to navigate future challenges adeptly.</p> <p>Emerging educational technologies have been invaluable allies to inquiry-based learning. They offer diverse information access, fortify problem contexts, provide timely feedback and learning assistance to function as a catalyst to boost students' motivation, and then support the cultivation of higher order thinking skills (Blumenfeld et al., [<reflink idref="bib4" id="ref7">4</reflink>]; Chen et al., [<reflink idref="bib11" id="ref8">11</reflink>]; Jonassen, [<reflink idref="bib40" id="ref9">40</reflink>]; Wang et al., [<reflink idref="bib75" id="ref10">75</reflink>]). Consequently, technology has become an integral component of IBL in university curriculums (Chan & Pow, [<reflink idref="bib8" id="ref11">8</reflink>]; Poitras & Lajoie, [<reflink idref="bib64" id="ref12">64</reflink>]). Yet, a disparity exists. Not every student utilizes these tools as educators intend (Ellis & Bliuc, [<reflink idref="bib20" id="ref13">20</reflink>]). This discrepancy arises from individual learning preferences and varying perceptions of technology's educational role (Farjon et al., [<reflink idref="bib23" id="ref14">23</reflink>]; Inan & Lowther, [<reflink idref="bib36" id="ref15">36</reflink>]; Merkt et al., [<reflink idref="bib58" id="ref16">58</reflink>]). Distinctively, students often fall into one of two categories: Deep technology users and surface technology users (Ellis et al., [<reflink idref="bib21" id="ref17">21</reflink>]). Deep technology users harness tools proficiently, often aiming to deepen content understanding, while surface users employ them sparingly, primarily to lessen coursework demands (Ellis et al., [<reflink idref="bib21" id="ref18">21</reflink>]). The reasons for this difference in technology use may lie in that there is a digital gap between different students either in the technology beliefs or the actions (Bray & Tangney, [<reflink idref="bib5" id="ref19">5</reflink>]; Kormos & Wisdom, [<reflink idref="bib46" id="ref20">46</reflink>]). Literature suggests that the depth of technology engagement correlates with comprehension and engagement levels, influencing learning outcomes (Chen, [<reflink idref="bib10" id="ref21">10</reflink>]; Ellis & Bliuc, [<reflink idref="bib20" id="ref22">20</reflink>]; Nelson et al., [<reflink idref="bib60" id="ref23">60</reflink>]; Pan & Mow, [<reflink idref="bib62" id="ref24">62</reflink>]).</p> <p>Students' engagement—a reflection of their dedication and active participation in learning (Henrie et al., [<reflink idref="bib31" id="ref25">31</reflink>])—is a cornerstone of successful education (Rodgers, [<reflink idref="bib67" id="ref26">67</reflink>]). This engagement becomes particularly crucial in autonomous learning environments like inquiry-based learning (Karaoğlan, [<reflink idref="bib43" id="ref27">43</reflink>]; Yılmaz & Yılmaz, [<reflink idref="bib76" id="ref28">76</reflink>]) and when incorporating learning technologies (Bergdahl et al., [<reflink idref="bib3" id="ref29">3</reflink>]). However, maintaining such engagement in tech-driven learning is daunting (Chiu, [<reflink idref="bib13" id="ref30">13</reflink>]; Henrie et al., [<reflink idref="bib31" id="ref31">31</reflink>]). Therefore, comprehending the nexus between technological approaches and learning engagement is pivotal. By understanding these dynamics, educators can craft more effective technology-rich learning experiences, maximizing student outcomes.</p> <p>Prior research has indicated a reciprocal relationship: Students' technological approaches using in their learning process could shape their overall learning engagement (Ellis & Bliuc, [<reflink idref="bib20" id="ref32">20</reflink>]; Jonassen, [<reflink idref="bib41" id="ref33">41</reflink>]; Rashid & Asghar, [<reflink idref="bib66" id="ref34">66</reflink>]), which in turn can influence their higher order thinking skills (Alsowat, [<reflink idref="bib1" id="ref35">1</reflink>]; Kim et al., [<reflink idref="bib45" id="ref36">45</reflink>]). It posits that learning engagement, as a mediator, might bridge the gap between technological approaches and higher order thinking skills. Yet, the intermediary role of learning engagement, specifically within the technology-enhanced inquiry-based learning (T-IBL) context, remains under-researched, especially when discerning between deep and surface technology user impacts on higher order thinking skills. Understanding this mediation is vital; without it, educators lack guidance in leveraging technology within technology-enhanced inquiry-based learning context to optimize student engagement and higher order thinking skills. This study seeks to fill this knowledge void by examining the role of learning engagement and offering actionable insights for educators, aiming to refine students' technological interactions and cognitive development in technology-enhanced inquiry-based learning environments. By investigating how different technology usage approaches affect higher order thinking skills through learning engagement, this study can provide insights into how educational practices can be adapted to ensure that all students, regardless of their technological access, can benefit equally from technology-enhanced learning environments, which could narrow the digital divide in technology-enhanced inquiry-based learning context.</p> <hd id="AN0189592951-4">Research purpose</hd> <p>The primary objective of this study was to investigate the intermediary role of learning engagement in the nexus between technology usage approaches and higher order thinking skills in the technology-enhanced inquiry-based learning setting among college students.</p> <p>Furthermore, to guide our inquiry effectively, it's imperative to understand the current perceptions of college students regarding technology usage approaches, learning engagement, and higher order thinking skills within the technology-augmented inquiry-based learning framework. Possessing this knowledge will equip educators to guide surface technology users towards recognizing the full potential of technological tools, harnessing them more proficiently, and consequently enhancing their learning engagement and higher order thinking skills in the technology-enhanced inquiry-based learning environment.</p> <p>In light of the above, our research was steered by the following two main questions:</p> <p>Research Question 1 (RQ 1): How do college students perceive the approaches to technology use, learning engagement, and higher order thinking skills in the technology-enhanced inquiry-based learning setting?</p> <p>Research Question 2 (RQ 2): In the technology-enhanced inquiry-based learning setting, how does learning engagement act as a mediator between technology usage approaches and higher order thinking skills?</p> <hd id="AN0189592951-5">Literature review</hd> <p></p> <hd id="AN0189592951-6">The relationship between technology usage approaches and learning engagement</hd> <p>In order to achieve an environment in which an inquiry-based constructivist approach promotes the use of technology, changes in teaching methods and student learning experiences are needed, which fundamentally depend on the actions and beliefs of teachers and students (Bray & Tangney, [<reflink idref="bib5" id="ref37">5</reflink>]; Ellis & Bliuc, [<reflink idref="bib20" id="ref38">20</reflink>]). Previous researches indicated that digital natives can limit themselves to using only a small set of technologies (Kennedy et al., [<reflink idref="bib44" id="ref39">44</reflink>]; Thompson, [<reflink idref="bib74" id="ref40">74</reflink>]). Diving into the nuances of technology adoption among students, two distinct categories emerge based on their engagement with technology: Deep and surface technology users (Ellis et al., [<reflink idref="bib21" id="ref41">21</reflink>]). Deep users tend to harness technology as a powerful tool to augment their learning, diving deep into course material and drawing real-world correlations. In contrast, surface users are more mechanistic, primarily seeking technology as a means to diminish academic workload, often overlooking the technology's expansive learning potential (Ellis & Bliuc, [<reflink idref="bib20" id="ref42">20</reflink>]; Ellis et al., [<reflink idref="bib21" id="ref43">21</reflink>]). Thus, differentiating and navigating these user profiles becomes pivotal. Prior research has revealed that college students with a comprehensive grasp of technology often exhibit a profound understanding of the subject matter. In contrast, those with a cursory grasp of technology typically display a superficial understanding of the content (Ellis et al., [<reflink idref="bib21" id="ref44">21</reflink>]). This underscores the imperative to distinguish between deep and surface technology users and subsequently steer surface users towards a more nuanced appreciation of technology.</p> <p>Learning engagement paints a broad spectrum, ranging from the sheer effort invested by students to achieve academic milestones to the intricate emotions tied with the learning journey (Sun & Rueda, [<reflink idref="bib71" id="ref45">71</reflink>]). Learning engagement refers to the learners entering a sustained, positive emotional and fulfilling mental state during the learning process (Luo et al., [<reflink idref="bib57" id="ref46">57</reflink>]). Learning engagement is a concrete index that reflects students' participation in the learning process. High commitment can positively predict academic achievement and negatively predict dropout (Sirin, [<reflink idref="bib70" id="ref47">70</reflink>]). It's a tri-dimensional space, encompassing behavioral, emotional, and cognitive dimensions. Among the three dimension, behavioral engagement consists of doing the work and following the rules; emotional engagement encompasses values, interest, and emotions; and cognitive engagement incorporates effort, motivation, and strategy use (Fredricks et al., [<reflink idref="bib26" id="ref48">26</reflink>], [<reflink idref="bib27" id="ref49">27</reflink>]). A close lens on these dimensions reveals students' active participation, emotional reactions to their learning environment, and the cognitive strategies deployed for knowledge acquisition (Jung & Lee, [<reflink idref="bib42" id="ref50">42</reflink>]).</p> <p>Past research has unfurled the intertwined relationship between technology use and learning engagement. While a superficial engagement with technology can stifle genuine learning, profound and purposeful use can amplify both engagement and motivation (Ellis & Bliuc, [<reflink idref="bib20" id="ref51">20</reflink>]; Rashid & Asghar, [<reflink idref="bib66" id="ref52">66</reflink>]). For instance, Pan and Mow's ([<reflink idref="bib62" id="ref53">62</reflink>]) insights highlighted that tech-savvy cohorts demonstrated superior learning engagement compared to traditional counterparts. These revelations champion the need for a robust integration of technology in education. Steered by these insights, we propose:</p> <p>Hypothesis 1 (H1): In a technology-enhanced, inquiry-based learning environment, college students' in-depth technology usage is directly correlated with increased learning engagement.</p> <p>Hypothesis 2 (H2): In the same context, superficial technology use by college students is inversely related to their learning engagement.</p> <hd id="AN0189592951-7">The relationship between learning engagement and HOTS</hd> <p>Higher order thinking skills is a key factor affecting students' learning outcomes and employment prospects (Huang et al., [<reflink idref="bib32" id="ref54">32</reflink>]). It is the cognitive ability to categorize, infer, generalize, and solve problems beyond given information in complex situations (Zhan et al., [<reflink idref="bib78" id="ref55">78</reflink>]). It is an umbrella term for various forms of thinking, including problem-solving, critical thinking and creativity (Hwang et al., [<reflink idref="bib35" id="ref56">35</reflink>]). Bloom's taxonomy's upper levels, notably analysis, synthesis, and evaluation, are frequently linked with higher order thinking skills (Yousef, [<reflink idref="bib77" id="ref57">77</reflink>]). The academic realm has heralded the significance of higher order thinking skills, deeming them indispensable for students as they navigate unforeseen challenges throughout life (Lu et al., [<reflink idref="bib53" id="ref58">53</reflink>], [<reflink idref="bib54" id="ref59">54</reflink>]; Miri et al., [<reflink idref="bib59" id="ref60">59</reflink>]; Tanujaya et al., [<reflink idref="bib73" id="ref61">73</reflink>]). Countries globally have rolled out educational reforms to bolster higher order thinking skills among students (Department of education, [<reflink idref="bib19" id="ref62">19</reflink>]; European Commission, [<reflink idref="bib22" id="ref63">22</reflink>]). A case in point is the European Union's S-TEAM initiative, earmarked to advance inquiry-based science teaching, thereby enriching higher order thinking skills. Various self-directed learning techniques like problem-based learning, inquiry-based learning, and case-based learning (Aslan, [<reflink idref="bib2" id="ref64">2</reflink>]; Lu et al., [<reflink idref="bib53" id="ref65">53</reflink>], [<reflink idref="bib54" id="ref66">54</reflink>]; Sadeh & Zion, [<reflink idref="bib68" id="ref67">68</reflink>]) have been tapped into, with a spotlight on inquiry-based learning in our study's context.</p> <p>Considering the relationship between higher order thinking skills and learning engagement, evidence suggests that in the flipped classroom context, there is a positive correlation between learning engagement and students' HOTS (Alsowat, [<reflink idref="bib1" id="ref68">1</reflink>]). Furthering this, Kim et al. ([<reflink idref="bib45" id="ref69">45</reflink>]) discovered a similar positive relationship between college students' engagement and higher order thinking skills, specifically when mobile technologies were utilized. This correlation was reaffirmed by Huang et al. ([<reflink idref="bib33" id="ref70">33</reflink>]) within a business simulation game learning context. It is observed that students who immerse themselves more deeply in learning activities tend to invest consistent mental and physical efforts in their learning journey, aiming for optimal outcomes (Liu et al., [<reflink idref="bib51" id="ref71">51</reflink>]). This suggests that students' active participation is likely to culminate in a richer learning experience. Drawing from the insights of the studies mentioned, we put forth the following hypothesis:</p> <p>Hypothesis 3 (H3): Within a technology-enhanced, inquiry-based learning setting, there is a direct correlation between college students' learning engagement and higher order thinking skills.</p> <hd id="AN0189592951-8">The relationship between technology usage approaches and HOTS</hd> <p>When it comes to the relationship between students' technology usage approaches and higher order thinking skills, previous studies found that students' deep approach to learning was associated with students' 'understanding, reflection, and critical thinking' (Phan, [<reflink idref="bib63" id="ref72">63</reflink>], p. 583). As critical thinking, problem solving and creativity are key items of higher order thinking skills (Hwang et al., [<reflink idref="bib35" id="ref73">35</reflink>]), it could be inferred that deep approach to learning is positively related to higher order thinking skills. Furthermore, Lee and Choi ([<reflink idref="bib48" id="ref74">48</reflink>]) also found that learners' higher-order thinking skills was directly and strongly influenced by deep learning approaches (Lee & Choi, [<reflink idref="bib48" id="ref75">48</reflink>]). It was also demonstrated by previous studies that students' deep learning approach was positively related to deep approaches to using technology (Ellis & Bliuc, [<reflink idref="bib20" id="ref76">20</reflink>]; Ellis et al., [<reflink idref="bib21" id="ref77">21</reflink>]). Based on the above analyses, we put forth the following hypothesis:</p> <p>Hypothesis 4 (H4): Within a technology-enhanced, inquiry-based learning setting, there is a direct and positive correlation between college students' deep approaches to using technology and higher order thinking skills.</p> <p>Hypothesis 5 (H5): Within a technology-enhanced, inquiry-based learning setting, there is a direct and negative correlation between college students' surface approaches to using technology and higher order thinking skills.</p> <p>While studies have explored the individual effects of technology usage, learning engagement, and higher order thinking skills, there exists a discernible gap in understanding their interconnected influence, especially in the context of technology-enhanced inquiry-based learning environment. Our study uniquely fills this gap by investigating the mediating role of learning engagement amidst technology approaches and higher order thinking skills. This granularity ensures that educators can create tailored pedagogical strategies to maximize student outcomes. By embedding our research questions and hypotheses within this framework, we aim to validate them by drawing a distinct line from established knowledge to the new insights our study offers. This research, therefore, does not just contribute incremental knowledge but provides pivotal insights that can redefine the way educators approach technology-integrated pedagogies.</p> <hd id="AN0189592951-9">The framework of the inquiry-based learning activity</hd> <p>The 5E Instructional Model was developed by Bybee et al. ([<reflink idref="bib7" id="ref78">7</reflink>]). It is a constructivist learning theory initially for science education and subsequent application to varied disciplines, including humanities and social sciences, and science and engineerings (Büyükkarci & Taşlidere, [<reflink idref="bib6" id="ref79">6</reflink>]; Jiang et al., [<reflink idref="bib39" id="ref80">39</reflink>]). The framework unfolds in five stages: Engagement, Exploration, Explanation, Elaboration, and Evaluation (Bybee et al., [<reflink idref="bib7" id="ref81">7</reflink>]). It has been employed in the previous studies to improve students' learning experiences and outcomes as a learning theory (Lu et al., [<reflink idref="bib56" id="ref82">56</reflink>]).</p> <p>Recognized for its efficacy in the inquiry-based learning context, the 5E framework has been broadly and consistently applied, producing positive impacts on education (Bybee et al., [<reflink idref="bib7" id="ref83">7</reflink>]). This study utilized the 5E theoretical framework as the foundation for the technology-enhanced inquiry-based learning. During the 5E stages, students and instructors moved through each phase recursively. In the <emph>engaged</emph> period, students and instructors make connections to prior knowledge and experiences and become engaged in the content. In the <emph>engaged</emph> period, they actively explore concepts through shared experiences. In the <emph>explain</emph> period, they begin to explain new understandings through process-oriented formative assessments. In the <emph>elaborate</emph> period, they practice using new knowledge as they elaborate their learning to unique contexts and situations. In the <emph>evaluate</emph> period, they evaluate their current conceptions and learning processes and products (Jeter et al., [<reflink idref="bib38" id="ref84">38</reflink>]).</p> <hd id="AN0189592951-10">Method</hd> <p></p> <hd id="AN0189592951-11">Research design</hd> <p>This study employed a quantitative-method approach to delve into students' HOTS, their technology usage approaches, and learning engagement. Quantitative research methods are systematic investigation processes that primarily involve the collection and analysis of numerical data to understand phenomena, establish patterns, test hypotheses, and make predictions. Furthermore, we aimed to examine the intermediary role of learning engagement in the relationship between technology usage approaches and HOTS within a technology-enhanced, inquiry-based learning framework. We utilized questionnaires for data collection and applied statistical techniques to understand the relationships between the research variables. Ethical approval for the research was granted by the committee, details of which have been concealed in line with the double-blind policy.</p> <hd id="AN0189592951-12">Participants</hd> <p>In this study, 160 students across four classes were chosen using simple random sampling, where every subset of a population has an equal chance of being picked. Not partitioning or subdividing the frame ensures each member has an identical likelihood of selection. Our study sample consisted of students from the experimental university who shared common characteristics: All 160 were Chinese, university students, and were immersed in a technology-enhanced, inquiry-based learning environment.</p> <p>These participants were enrolled in a course titled "Colorful World in the Classic Poetry Garden" at a research-oriented, four-year normal university situated in central China. Prior to initiating the study, consent was obtained from the participants and clearance was secured from the university's review board. Due to incomplete submissions, twenty responses were discarded, leading to an effective response rate of 87.5%. The gender distribution predominantly comprised females, with a ratio of roughly 3 females to every male, which is a common demographic distribution in normal Chinese universities. The selected university is known for its significant commitment to and emphasis on technology-enhanced learning.</p> <p>The course instructor possessed over a decade of teaching experience, with a robust background in weaving technology into teaching methodologies. Spanning a semester, the course ran for about twelve weeks. During this period, the instructor and students convened in a classroom for a 90-min session each week. In this curriculum, every inquiry-based learning activity, whether inside or outside the classroom, was supported by the Xuexitong APP.</p> <hd id="AN0189592951-13">The technology-enhanced inquiry-based learning context</hd> <p>To facilitate these inquiry-based learning activities, the Xuexitong app played an instrumental role, aiding both educators and learners. Revered and widely adopted by Chinese higher education institutions, educators, and students, Xuexitong (Guo, [<reflink idref="bib29" id="ref85">29</reflink>]) serves as a dynamic classroom response system, akin to renowned learning management systems like Blackboard or Moodle. The platform enables students to engage by responding to questions in real-time via their smartphones (Cox, [<reflink idref="bib16" id="ref86">16</reflink>]).</p> <p>The versatility of Xuexitong is evident in its comprehensive suite of features tailored to address both educators' pedagogical requirements and students' learning aspirations. Key features include: A diverse repository of teaching and learning resources, accessible to both educators and learners; An omnipresent virtual classroom, fostering interaction among peers and between students and instructors; Real-time analytics on student performance, presented through an intuitive interface; An expansive array of question formats and response submission methods. As they transitioned through each phase of inquiry, both instructors and students had the flexibility to select suitable functions within the app to optimize their teaching and learning experiences.</p> <p>When conducting the technology-enhanced inquiry-based learning activities, the App Xuexitong was used throughout the whole class. Before the class, students signed in via the app to indicate that they have arrived in the classroom. During the class, the instructor would release the course materials to all students through the app, and students could mark the course materials while learning them through the platform. When the instructor initiated a question, students could grab the opportunity to answer through the platform. The instructor would also initiate discussions through the platform, and each group could have discussions in its own space in the app. Groups can see each other's discussion process, groups could comment and like, and finally vote for the best discussion group. After the class, students could continue the discussion through the app, submit homework, communicate with classmates and instructors and so on.</p> <p>Throughout these technology-aided IBL activities, students progressively navigated the 5E phases. During the Engagement stage, the instructor primed students with the new content by linking it to their existing knowledge and experiences. At the Exploration phase, students delved into the new content via shared experiential learning. In the Explanation phase, learners showcased their newfound understanding through formative, process-centric assessments. The Elaboration phase saw students applying the freshly acquired content to diverse contexts. Finally, in the Evaluation phase, a holistic assessment of students' conceptions, learning journey, and outcomes was conducted (Jeter et al., [<reflink idref="bib38" id="ref87">38</reflink>]).</p> <hd id="AN0189592951-14">Instruments</hd> <p>In this research, we utilized three distinct questionnaires to gauge students' higher order thinking skills, their approaches to using technology, and their level of learning engagement.</p> <hd id="AN0189592951-15">Higher order thinking skills (HOTS)</hd> <p>The HOTS instrument leveraged for this study was devised by Hwang et al. ([<reflink idref="bib35" id="ref88">35</reflink>]). This tool encompasses a tri-dimensional construct consisting of 11 items: Problem-solving (four items), critical thinking (four items), and creativity (three items). Items are evaluated on a five-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree). An illustrative item for critical thinking reads, "During this technology-enhanced inquiry activity, I periodically assess if I'm meeting my objectives."</p> <hd id="AN0189592951-16">Approaches to using technologies</hd> <p>To measure students' technological approaches, we adopted the SAOLT instrument, formulated by Ellis and Bliuc ([<reflink idref="bib20" id="ref89">20</reflink>]). This scale possesses two key dimensions encompassing eleven items: Deep approaches to using technologies (six items) and surface approaches to using technologies (five items). Items are again scored on a five-point Likert scale, from 1 (strongly disagree) to 5 (strongly agree). An exemplar item for deep technology usage is, "To gain a comprehensive grasp of key concepts in this technology-enhanced inquiry activity, I make an effort to utilize the online learning tools provided in this course."</p> <hd id="AN0189592951-17">Learning engagement</hd> <p>The tool measuring student engagement is rooted in initial work by Fredricks et al., ([<reflink idref="bib26" id="ref90">26</reflink>], [<reflink idref="bib27" id="ref91">27</reflink>]) and was later refined by Sun and Rueda ([<reflink idref="bib71" id="ref92">71</reflink>]). This scale highlights three pivotal dimensions: Emotional, cognitive, and behavioral engagement. The behavioral dimension contains three items, the emotional has seven, and the cognitive comprises five. Each item is rated on a scale from 1 (strongly disagree) to 5 (strongly agree). An exemplary item from the behavioral engagement section is, "I consistently complete my assignments punctually for this technology-enhanced inquiry activity course."</p> <p>In our study, Likert scales were employed as a means to gauge students' perceptions of the key variables under investigation (Creswell, [<reflink idref="bib17" id="ref93">17</reflink>]). While it is generally prudent to treat Likert scale data as ordinal due to their categorical nature, we chose to treat them as continuous variables in this context and provide a comprehensive justification for this approach.</p> <p>Firstly, it is important to note that the Likert scale items we employed were designed to measure the same underlying construct consistently across multiple dimensions. Each dimension represented a set of related questions, and the Likert scale responses were then averaged to derive a single value for each dimension. This process not only provided a more comprehensive view of students' attitudes and opinions but also yielded continuous values that can be analyzed statistically. The aggregation of Likert scale items in this manner helps reduce the potential loss of information associated with treating them as purely ordinal (Creswell, [<reflink idref="bib17" id="ref94">17</reflink>]).</p> <p>Furthermore, the decision to treat Likert scales as continuous is supported by the precedent set in the field of educational research. Several recent studies, including notable works by Lin and Wang ([<reflink idref="bib49" id="ref95">49</reflink>]) and Lu et al. ([<reflink idref="bib55" id="ref96">55</reflink>]), have successfully employed this approach in similar contexts, such as Wiki-based PBL internship courses and technology-enhanced open inquiry-based learning. These studies have demonstrated that treating Likert scale data as continuous variables can provide valuable insights into the relationships between key research variables. On top of this, Jaccard and Wan ([<reflink idref="bib37" id="ref97">37</reflink>]) indicated that the errors for treating the Likert scale results as interval data are minimal, further supporting the appropriateness of this approach in this study.</p> <p>It is important to acknowledge that while Likert scales are not truly continuous in a mathematical sense, treating them as such in our study allows for the application of parametric statistical techniques, which are robust and widely accepted in the field. This choice was made to enable us to explore the relationships between variables more effectively, given the nature of our data.</p> <p>In conclusion, our decision to treat Likert scale data as continuous in this study is based on the specific design of our survey instrument, the aggregation of Likert items across dimensions, the precedent set by recent educational research, and the minimal errors associated with this approach, as indicated by Jaccard and Wan ([<reflink idref="bib37" id="ref98">37</reflink>]). We believe that this approach enhances the analytical power of our study and enables a more nuanced understanding of students' perceptions of the key variables.</p> <hd id="AN0189592951-18">Data collection and analysis</hd> <p>Towards the end of the semester, student feedback regarding higher order thinking skills, their technological usage approaches, and learning engagement was gathered during a class recess. Data collection leveraged the online questionnaire platform, https://www.wjx.cn, which is a prevalent tool in China, especially within academic environments like universities (Long et al., [<reflink idref="bib52" id="ref99">52</reflink>]). Completing the survey typically took between 5–10 min. Prior to initiating the survey, students were informed that their responses would bear no influence on their academic grades. Participation was voluntary, ensuring anonymity for all respondents. Additionally, students were guided to reflect on the technology-enhanced inquiry-based learning context when addressing the survey items.</p> <p>Subsequent to collection, the data was imported into both SPSS and SmartPLS for thorough analysis. In the present study, the Partial Least Square (PLS) approach of Structural Equation Modeling (SEM), facilitated by SmartPLS, was employed to delve into the intermediary role of learning engagement linking students' technology usage approaches and higher order thinking skills.</p> <hd id="AN0189592951-19">Results</hd> <p></p> <hd id="AN0189592951-20">Preliminary analysis</hd> <p>To verify the hypothesized research model, Partial least square (PLS) method was used. PLS proved appropriate for the sample size of this study (Chin, [<reflink idref="bib12" id="ref100">12</reflink>]; Gefen et al., [<reflink idref="bib28" id="ref101">28</reflink>]) and well-suited for testing theories in early stages of development (Fornell & Bookstein, [<reflink idref="bib24" id="ref102">24</reflink>]). SmartPLS 4 software was used to assess the measurement and structural models.</p> <p>Participants' mean scores and standard deviations of each scale are observable in Table 1. The results showed that participants' surface approaches to using technologies (M = 2.67, SD =.86) was found to be the lowest score, while the students' higher order thinking skills (M = 3.97, SD =.48) was the highest. This was followed by deep approaches to using technologies (M = 3.96, SD =.55), and learning engagement (M = 3.89, SD =.45). Overall, the mean score of each dimension measured exceeded the set median of 3 except surface approaches to using technologies.</p> <p>Table 1 Descriptive statistics</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left" /><th align="left"><p>M</p></th><th align="left"><p>SD</p></th></tr></thead><tbody><tr><td align="left"><p>DA</p></td><td char="." align="char"><p>3.96</p></td><td char="." align="char"><p>.55</p></td></tr><tr><td align="left"><p>SA</p></td><td char="." align="char"><p>2.67</p></td><td char="." align="char"><p>.86</p></td></tr><tr><td align="left"><p>LE</p></td><td char="." align="char"><p>3.89</p></td><td char="." align="char"><p>.45</p></td></tr><tr><td align="left"><p>HOTS</p></td><td char="." align="char"><p>3.97</p></td><td char="." align="char"><p>.48</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>DA</emph> Deep approaches to using technologies, <emph>SA</emph> Surface approaches to using technologies, <emph>LE</emph> Learning engagement, HOTSHigher-order thinking skills</p> <hd id="AN0189592951-21">Evaluation of the measurement model</hd> <p>To answer the RQ 2, firstly, the reliability of measures, convergent validity, and discriminant validity of the research model were assessed.</p> <p>Table 2 showcase the reliability assessment of the measurement model, utilizing both composite reliability and Cronbach's alpha. The composite reliability (CR) coefficients exceeded the 0.7 threshold in all dimensions, indicating satisfactory reliability as per Nunnally and Bernstein ([<reflink idref="bib61" id="ref103">61</reflink>]). Furthermore, all Cronbach's alpha values surpassed.55, fitting within the accepted boundaries as established by Helmstadter ([<reflink idref="bib30" id="ref104">30</reflink>]). To gauge convergent validity, the average variance extracted (AVE) was employed. The AVE values surpassed the.5 benchmark in all dimensions, signifying satisfactory convergent validity as per Segars ([<reflink idref="bib69" id="ref105">69</reflink>]).</p> <p>Table 2 Results of the measurement model</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left" rowspan="2" /><th align="left" colspan="2"><p>Reliability</p></th><th align="left"><p>Convergent validity</p></th><th align="left" colspan="4"><p>Discriminant validity</p></th></tr><tr><th align="left"><p>Alpha</p></th><th align="left"><p>CR</p></th><th align="left"><p>AVE</p></th><th align="left"><p>DA</p></th><th align="left"><p>HOTS</p></th><th align="left"><p>LE</p></th><th align="left"><p>SA</p></th></tr></thead><tbody><tr><td align="left"><p>DA</p></td><td char="." align="char"><p>.882</p></td><td char="." align="char"><p>.910</p></td><td char="." align="char"><p>.627</p></td><td char="." align="char"><p><bold>.791</bold></p></td><td char="." align="char" /><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>HOTS</p></td><td char="." align="char"><p>.888</p></td><td char="." align="char"><p>.910</p></td><td char="." align="char"><p>.529</p></td><td char="." align="char"><p>.782</p></td><td char="." align="char"><p><bold>.727</bold></p></td><td char="." align="char" /><td char="." align="char" /></tr><tr><td align="left"><p>LE</p></td><td char="." align="char"><p>.808</p></td><td char="." align="char"><p>.886</p></td><td char="." align="char"><p>.723</p></td><td char="." align="char"><p>.706</p></td><td char="." align="char"><p>.693</p></td><td char="." align="char"><p><bold>.850</bold></p></td><td char="." align="char" /></tr><tr><td align="left"><p>SA</p></td><td char="." align="char"><p>.878</p></td><td char="." align="char"><p>.885</p></td><td char="." align="char"><p>.661</p></td><td char="." align="char"><p>.182</p></td><td char="." align="char"><p>.163</p></td><td char="." align="char"><p>.209</p></td><td char="." align="char"><p><bold>.813</bold></p></td></tr><tr><td align="left"><p>Criteria</p></td><td char="." align="char"><p> >.55</p></td><td char="." align="char"><p> >.70</p></td><td char="." align="char"><p> >.50</p></td><td char="." align="char" colspan="4" /></tr></tbody></table> </ephtml> </p> <p> <emph>DA</emph> Deep approaches to using technologies, <emph>SA</emph> Surface approaches to using technologies, <emph>LE</emph> Learning engagement, <emph>HOTS</emph> Higher-order thinking skills Bold numbers represent square roots of the AVE</p> <p>Furthermore, to evaluate the discriminant validity, the square roots of AVE were compared to correlations among latent variables (Fornell & Larcker, [<reflink idref="bib25" id="ref106">25</reflink>]), in which all latent correlations were less than the corresponding AVE square roots. In sum, the adequacy of the measurement model indicated that all the items were reliable indicators of the hypothesized measures.</p> <hd id="AN0189592951-22">The structural model and hypothesis test</hd> <p>To verify the research hypotheses in the present study, the Partial Least Square (PLS) path modelling estimation was conducted, and the result is shown in Fig. 1. In this model, path coefficients along with the associated t-values were provided and explained the variance given.</p> <p>Graph: Fig. 1 The structural model. DA Deep approaches to using technologies, SA Surface approaches to using technologies, LE Learning engagement, HOTS Higher-order thinking skills</p> <p>The results of paths testing in the present study are reported in Table 3. It is evident that students' deep approach to using technology is significantly and positively associated with learning engagement and higher order thinking skills. In addition, learning engagement is significantly and positively related to higher order thinking skills. In contrast, students' surface approach to using technology is negatively associated with learning engagement and higher order thinking skills and these associations are not statistically significant. That's to say, H1, H3 and H4 were supported, while H2 and H5 were rejected. In other words, students' learning engagement is found to be a mediating variable between deep approach to using technology and higher order thinking skills.</p> <p>Table 3 Results of the hypothses</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left"><p>Hypotheses</p></th><th align="left"><p>Path</p></th><th align="left"><p>Path coefficient</p></th><th align="left"><p>t-value</p></th><th align="left"><p>Results</p></th></tr></thead><tbody><tr><td align="left"><p>H1</p></td><td align="left"><p>DA → LE</p></td><td char="." align="char"><p>.593</p></td><td char="." align="char"><p>8.668***</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H2</p></td><td align="left"><p>SA → LE</p></td><td char="." align="char"><p>−.041</p></td><td char="." align="char"><p>.426</p></td><td align="left"><p>Rejected</p></td></tr><tr><td align="left"><p>H3</p></td><td align="left"><p>LE → HOTS</p></td><td char="." align="char"><p>.259</p></td><td char="." align="char"><p>2.875**</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H4</p></td><td align="left"><p>DA → HOTS</p></td><td char="." align="char"><p>.547</p></td><td char="." align="char"><p>13.802***</p></td><td align="left"><p>Supported</p></td></tr><tr><td align="left"><p>H5</p></td><td align="left"><p>SA → HOTS</p></td><td char="." align="char"><p>−.015</p></td><td char="." align="char"><p>.287</p></td><td align="left"><p>Rejected</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>DA</emph> Deep approaches to using technologies, <emph>SA</emph> Surface approaches to using technologies, <emph>LE</emph> Learning engagement, <emph>HOTS</emph> Higher-order thinking skills ** <.01, ***p <.001.</p> <hd id="AN0189592951-23">Analysis of indirect and total effects among key factors</hd> <p>Further, the indirect effects of the hypothesized model were examined. As can be seen in Table 4, the indirect effect between students' deep approach to using technology and higher order thinking skills was positive and significant, while the indirect effect between students' surface approach to using technology and higher order thinking skills was negative but not significant.</p> <p>Table 4 Results of the indirect effects</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left" /><th align="left"><p>M</p></th><th align="left"><p>STDEV</p></th><th align="left"><p>T</p></th><th align="left"><p>P</p></th></tr></thead><tbody><tr><td align="left"><p>DA- > HOTS</p></td><td char="." align="char"><p>.144</p></td><td char="." align="char"><p>.056</p></td><td char="." align="char"><p>2.744</p></td><td char="." align="char"><p>.006</p></td></tr><tr><td align="left"><p>SA- > HOTS</p></td><td char="." align="char"><p>−.012</p></td><td char="." align="char"><p>.025</p></td><td char="." align="char"><p>.415</p></td><td char="." align="char"><p>.678</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>DA</emph> Deep approaches to using technologies, <emph>SA</emph> Surface approaches to using technologies, <emph>HOTS</emph> Higher-order thinking skills</p> <p>As can be seen in Table 5, the total effect of deep approach to using technology on higher order thinking skills was.701, suggesting that deep approach to using technology has a strong and significant influence on higher order thinking skills, both directly and partially mediated by learning engagement. As both the direct and indirect paths are significant, this supports a partial mediation model. While the total effect of surface approach to using technology on higher order thinking skills was minimal and not significant, indicating that surface approach to using technology does not have a meaningful impact on higher order thinking skills, either directly or indirectly through learning engagement.</p> <p>Table 5 Analysis of indirect and total effects between key factors</p> <p> <ephtml> <table rules="groups"><thead><tr><th align="left" /><th align="left"><p>Path</p></th><th align="left"><p>Effect value</p></th></tr></thead><tbody><tr><td align="left" colspan="3"><p>DA- > HOTS</p></td></tr><tr><td align="left"><p>Direct effect</p></td><td align="left"><p>DA- > HOTS</p></td><td align="left"><p>.547</p></td></tr><tr><td align="left"><p>Indirect effect</p></td><td align="left"><p>DA- > LE- > HOTS</p></td><td align="left"><p>.593*.259 =.154</p></td></tr><tr><td align="left"><p>Total effect</p></td><td align="left" /><td align="left"><p>.701</p></td></tr><tr><td align="left" colspan="3"><p>SA- > HOTS</p></td></tr><tr><td align="left"><p>Direct effect</p></td><td align="left"><p>SA- > HOTS</p></td><td align="left"><p>−.015</p></td></tr><tr><td align="left"><p>Indirect effect</p></td><td align="left"><p>SA- > LE- > HOTS</p></td><td align="left"><p>−.041 ×.259 = −.011</p></td></tr><tr><td align="left"><p>Total effect</p></td><td align="left" /><td align="left"><p>−.026</p></td></tr></tbody></table> </ephtml> </p> <p> <emph>DA</emph> Deep approaches to using technologies, <emph>SA</emph> Surface approaches to using technologies, <emph>LE</emph> Learning engagement, <emph>HOTS</emph> Higher-order thinking skills ** <.01, ***p <.001.</p> <hd id="AN0189592951-24">Discussion and conclusion</hd> <p>To date, very few empirical studies have specifically investigated the relationship between students' technology using approaches and higher order thinking skills in the technology-enhanced inquiry-based learning context, especially the mediating role of learning engagement. Results of the study show that there are both direct and indirect relationship between students' technology using approaches and higher order thinking skills.</p> <p> <emph>DA</emph> Deep approaches to using technologies, <emph>SA</emph> Surface approaches to using technologies, <emph>HOTS</emph> Higher-order thinking skills</p> <p>First, both the relationships between students' deep approach to using technology and learning engagement and higher order thinking skills were discussed. It was found that students' deep approach to using technology was significant positively related to learning engagement and higher order thinking skills. This finding is consistent with results from previous studies (Ellis & Bliuc, [<reflink idref="bib20" id="ref107">20</reflink>]; Lee & Choi, [<reflink idref="bib48" id="ref108">48</reflink>]; Pan & Mow, [<reflink idref="bib62" id="ref109">62</reflink>]; Rashid & Asghar, [<reflink idref="bib66" id="ref110">66</reflink>]). The finding indicates that it is necessary to strengthen the depth of students' technical use. Especially now, with generative AI all the rage, everyone should learn how to work with it to improve the learning and productivity (Chiu, [<reflink idref="bib14" id="ref111">14</reflink>]).</p> <p>Second, both the relationships between students' surface approach to using technology and learning engagement and higher order thinking skills were explored. It was found that students' surface approach to using technology was not significantly associated with learning engagement and higher order thinking skills. Also, it was found to have negative relation to these two factors. This finding is consistent with results from previous studies (Ellis & Bliuc, [<reflink idref="bib20" id="ref112">20</reflink>]; Rashid & Asghar, [<reflink idref="bib66" id="ref113">66</reflink>]). The finding offers new insights when interpreted through the lens of cognitive load theory (Sweller, [<reflink idref="bib72" id="ref114">72</reflink>]). Specifically, a surface approach often involves fragmented, shallow interactions with technology that increase extraneous cognitive load, distracting students from engaging meaningfully with learning content. Instead of promoting intrinsic cognitive engagement necessary for analysis, synthesis, and evaluation, surface use overwhelms working memory with irrelevant or superficial tasks, thus, inhibiting the development of higher-order thinking skills. This finding underscores the need to design technology-integrated learning experiences that minimize unnecessary cognitive demands and encourage sustained, meaningful interaction with digital tools.</p> <p>Last but not least, the mediating role of learning engagement between students' technology usage approaches and higher order thinking skills was also examined. It was found that learning engagement was significant positively related to higher order thinking skills. This finding is consistent with previous studies (Alsowat, [<reflink idref="bib1" id="ref115">1</reflink>]; Kim et al., [<reflink idref="bib45" id="ref116">45</reflink>]). In other words, learning engagement acts as a mediating variable between students' deep approach to using technology and higher order thinking skills, but not between students' surface approach and higher order thinking skills. This can be further explained from the perspective of self-determination theory (Deci & Ryan, [<reflink idref="bib18" id="ref117">18</reflink>]), particularly in terms of the quality of learning motivation. A deep approach to technology usage tends to activate autonomous motivation, characterized by a genuine interest and internalized value for learning tasks, which in turn enhances engagement across cognitive, emotional, and behavioral dimensions. Such high-quality engagement becomes the driving force behind the cultivation of higher-order thinking skills. Conversely, a surface approach is often associated with controlled motivation or even amotivation, resulting in minimal or inauthentic engagement that fails to translate into cognitive gains. This finding emphasizes that fostering authentic, self-driven engagement is a critical pathway through which deep technology use can effectively enhance students' higher-order thinking capabilities in technology-enhanced inquiry-based environments.</p> <p>Additionally, even as all the participants employed similar technological tools in the same curriculum, different approaches to using technologies may resulted in different learning outcomes. This underscores the notion that a single technological tool may not cater universally. Tailored support for specific groups is paramount (Nelson et al., [<reflink idref="bib60" id="ref118">60</reflink>]). One potential remedy is diversifying the technological tools available, enabling students to identify and leverage tools best suited for their inquiry-based tasks. Another approach emphasizes educating both educators and students that the integration of technology should be rooted in pedagogical objectives, not merely driven by the allure of technology. Such an approach would ensure thoughtful and purposeful tech integration in educational endeavors (Farjon et al., [<reflink idref="bib23" id="ref119">23</reflink>]).</p> <hd id="AN0189592951-25">Theoretical and pedagogical value</hd> <p>The outcomes of this research offer valuable theoretical and practical insights for future studies focusing on technology-enhanced inquiry-based learning. Theoretically, the study contributes in three important ways. First, it highlights the result that the depth of technology usage, specifically the deep approach is a key predictor of learning engagement and higher-order thinking skills, underscoring that not all technology usage is equally beneficial for cognitive development. Second, it refines that the theoretical understanding of technology integration by showing that a surface approach to technology usage neither promotes engagement nor higher-order cognitive outcomes, challenging the assumption that technology access alone drives meaningful learning. Third, it identifies that learning engagement acts as a mediator between deep technology usage and higher-order thinking skills, but not between surface technology use and higher order thinking skills. This shows how technology usage affects learning and points out that only when students use technology in a meaningful and engaged way can it improve their higher order thinking skills. Overall, these findings call for future theoretical models to differentiate between qualitative modes of technology use and to incorporate engagement as a central construct in explaining technology's educational impact.</p> <p>Pedagogically, these findings imply that it is needed for educators to deliberately design learning experiences that encourage a deep approach to technology use. For example, when assigning structures, classroom activities, and technology tool selections, it is needed to foster sustained engagement, critical inquiry, and reflective thinking rather than surface-level interaction. For students those who inclined toward superficial technology use, some structured interventions (including setting clear learning objectives, scaffolding inquiry processes, and providing opportunities for self-directed exploration) could be useful to guide them toward deeper technology engagement.</p> <p>Furthermore, the study underscores the necessity of professional development programs that extend beyond technical skills training. Educators need to be equipped with strategies for cultivating meaningful learning engagement in technology-rich environments. At the institutional level, creating a learning culture that values deep, purposeful technology use over passive or instrumental adoption is crucial for maximizing educational outcomes.</p> <p>Finally, by clarifying the mediating role of learning engagement between technology use approaches and higher-order thinking skills, this research addresses a notable gap in the literature and offers an empirically grounded framework for designing more effective T-IBL environments. It advocates for shifting the focus from technology-centered integration to student engagement-centered integration, ensuring that technology serves as a catalyst for deeper cognitive development rather than merely an external enhancement.</p> <hd id="AN0189592951-26">Limitations and future research directions</hd> <p>While the findings of this study offer valuable insights, several limitations warrant mention. Firstly, the results are based on a relatively confined sample size and are specific to a singular subject, potentially narrowing the breadth of their applicability. Future studies would benefit from a more expansive sample for wider generalizability. Secondly, the reliance on subjective, self-reported data is a limitation. Future studies might consider integrating objective metrics, such as observational data on learning behaviors (e.g. which functions of the app students used and for what purposes), to enhance the richness and accuracy of findings.</p> <p>Additionally, future inquiries could delve into the efficacy of introducing specific interventions, such as technology workshops. For example, research might investigate how a workshop on augmented reality tools impacts students' engagement and learning outcomes compared to traditional methods. The rapid evolution of education technologies, like virtual reality or artificial intelligence, also warrants exploration. One possible direction could be assessing how utilizing virtual reality simulations in a biology class influences students' grasp of complex concepts, such as cellular processes, compared to textbook-based learning.An examination of the role of educators in technology adoption might consider how teacher training programs equip educators with the skills to integrate cutting-edge tools into their curriculum and its resultant effect on student performance. Investigating the intersection of socio-economic factors with tech usage could provide insights on how students from different economic backgrounds have varied access to technology, and therefore, varied learning experiences. Lastly, the potential for customized technology-enhanced learning pathways based on student categorizations can be examined. For instance, studies might design technology-enhanced modules specifically tailored for "deep technology users" and "surface technology users" to ascertain if such customizations lead to improved HOTS outcomes for both groups. These directions can further enrich our understanding of technology's role in modern pedagogy.</p> <hd id="AN0189592951-27">Acknowledgements</hd> <p>NA.</p> <hd id="AN0189592951-28">Funding</hd> <p>This work was supported by the Major Project of Philosophy and Social Science Research in Colleges and Universities of Jiangsu Province, "Research on hierarchical collaborative inquiry-based teaching styles based on the smart education platform of primary and secondary schools in Jiangsu" (Grant No. 2024SJZD074), the Education Science Planning Project of Jiangsu Province, "Research on the mechanism and intervention of AI-supported collective intelligence in collaborative inquiry-based learning" (Grant No. C/2023/01/108), the National Natural Science Foundation of China (Grant No. 62407023).</p> <hd id="AN0189592951-29">Data availability</hd> <p>The datasets generated and analyzed during the current study are not publicly available but will be provided by the corresponding author on reasonable request.</p> <hd id="AN0189592951-30">Declarations</hd> <p></p> <hd id="AN0189592951-31">Conflict of interests</hd> <p>The authors declare that there are no conflicts of interest.</p> <hd id="AN0189592951-32">Appendix A</hd> <p>Participants' higher order thinking skills, approaches to using technology, and their level of learning engagement are assessed based on specific criteria. Participants rate each item on a 5-point Likert-type scale, which ranges from 1 (strongly disagree) to 5 (strongly agree). Below is a breakdown of these criteria:</p> <hd id="AN0189592951-33">Higher order thinking skills</hd> <p>Problem-solving.</p> <p>1. When facing problems, I believe I have the ability to solve them.</p> <ulist> <item>2. I believe I can put effort into solving problems.</item> <item>3. I can solve problems that I have met before.</item> <item>4. I am willing to face problems and make an effort to solve them.</item> </ulist> <p>Critical thinking.</p> <p>1. I ask myself periodically if I am meeting my goals.</p> <ulist> <item>2. I consider several alternatives to a problem before I answer.</item> <item>3. I find myself pausing regularly to check my comprehension.</item> <item>4. I ask myself questions about how well I am doing once I finish a task.</item> </ulist> <p>Creativity.</p> <p>1. I like to observe something I haven't seen before and understand it in detail.</p> <ulist> <item>2. I like to try something new.</item> <item>3. I like to do something by myself.</item> </ulist> <hd id="AN0189592951-34">Approaches to using technology</hd> <p>Deep approaches to using technologies.</p> <p>1. I find I use the online learning technologies in this course to further my research into a topic.</p> <ulist> <item>2. I spend time using the online learning technologies in this course to develop my knowledge on key topics.</item> <item>3. I try to use the online learning technologies in this course to achieve a more complete understanding of key concepts.</item> <item>4. I find interacting with online learning technologies in this course promotes deeper understanding of key ideas.</item> <item>5. I try to use the online learning technologies in this course to communicate with other participants to test my ideas.</item> <item>6. I find using the online learning technologies in this course help me to develop my critical thinking.</item> </ulist> <p>Surface approaches to using technologies.</p> <p>1. I use online learning technologies in this course mainly to download files.</p> <ulist> <item>2. I restrict my use of online learning technologies in this course to do as little as possible.</item> <item>3. I do not use the online learning technologies in this course to enable me to achieve my goals.</item> <item>4. I only use the online learning technologies in this course to fulfil course requirements.</item> <item>5. I do not find using online technologies in this course helps me to understand things more deeply.</item> </ulist> <hd id="AN0189592951-35">Learning engagement</hd> <p>Behavior engagement.</p> <p>1. I follow the rules of the online class.</p> <ulist> <item>2. I complete my homework on time.</item> <item>3. I check my schoolwork for mistakes.</item> </ulist> <p>Emotional engagement.</p> <p>1. I like taking the online class.</p> <ulist> <item>2. I feel excited by my work at the online class.</item> <item>3. The online classroom is a fun place to be.</item> <item>4. I am interested in the work at the online class.</item> <item>5. I feel happy when taking online class.</item> <item>6. I feel bored by the online class.</item> <item>7. I talk with people outside of school about what I am learning in the online class.</item> </ulist> <p>Cognitive engagement.</p> <p>1. I study at home even when I do not have a test.</p> <ulist> <item>2. I try to look for some course-related information on other resources such as television, journal papers, magazines, etc.</item> <item>3. When I read the course materials, I ask myself questions to make sure I understand what it is about.</item> <item>4. I read extra materials to learn more about things we do in the online class.</item> <item>5. If I do not know about a concept when I am learning in the online class, I do something to figure it out.</item> </ulist> <hd id="AN0189592951-36">Publisher's Note</hd> <p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p> <ref id="AN0189592951-37"> <title> References </title> <blist> <bibl id="bib1" idref="ref35" type="bt">1</bibl> <bibtext> Alsowat H. An EFL flipped classroom teaching model: Effects on English language higher-order thinking skills, student engagement and satisfaction. Journal of Education and Practice. 2016; 7; 9: 108-121</bibtext> </blist> <blist> <bibl id="bib2" idref="ref64" type="bt">2</bibl> <bibtext> Aslan A. Problem-based learning in live online classes: Learning achievement, problem-solving skill, communication skill, and interaction. 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Her research interests include artificial intelligence, higher order thinking skills and technology-enhanced learning.</p> <p>Tongling Ji Tongling Ji is a graduate student of the School of Business at Nanjing University, China. Her research interests include artificial intelligence applications in education.</p> <p>Feng Lu Feng Lu is a professor of the College of Education Science and Technology at Nanjing University of Posts and Telecommunications, China. His research interests include digital literacy and educational technology.</p> <p>Rustam Shadiev Rustam Shadiev is a tenured professor at the College of Education, Zhejiang University, China. His research interests include technology-supported language learning and cross-cultural education.</p> </aug> <nolink nlid="nl1" bibid="bib15" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib34" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib47" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib50" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib65" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib11" firstref="ref8"></nolink> <nolink nlid="nl7" bibid="bib40" firstref="ref9"></nolink> <nolink nlid="nl8" bibid="bib75" firstref="ref10"></nolink> <nolink nlid="nl9" bibid="bib64" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib20" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib23" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib36" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib58" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib21" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib46" firstref="ref20"></nolink> <nolink nlid="nl16" bibid="bib10" firstref="ref21"></nolink> <nolink nlid="nl17" bibid="bib60" firstref="ref23"></nolink> <nolink nlid="nl18" bibid="bib62" firstref="ref24"></nolink> <nolink nlid="nl19" bibid="bib31" firstref="ref25"></nolink> <nolink nlid="nl20" bibid="bib67" firstref="ref26"></nolink> <nolink nlid="nl21" bibid="bib43" firstref="ref27"></nolink> <nolink nlid="nl22" bibid="bib76" firstref="ref28"></nolink> <nolink nlid="nl23" bibid="bib13" firstref="ref30"></nolink> <nolink nlid="nl24" bibid="bib41" firstref="ref33"></nolink> <nolink nlid="nl25" bibid="bib66" firstref="ref34"></nolink> <nolink nlid="nl26" bibid="bib45" firstref="ref36"></nolink> <nolink nlid="nl27" bibid="bib44" firstref="ref39"></nolink> <nolink nlid="nl28" bibid="bib74" firstref="ref40"></nolink> <nolink nlid="nl29" bibid="bib71" firstref="ref45"></nolink> <nolink nlid="nl30" bibid="bib57" firstref="ref46"></nolink> <nolink nlid="nl31" bibid="bib70" firstref="ref47"></nolink> <nolink nlid="nl32" bibid="bib26" firstref="ref48"></nolink> <nolink nlid="nl33" bibid="bib27" firstref="ref49"></nolink> <nolink nlid="nl34" bibid="bib42" firstref="ref50"></nolink> <nolink nlid="nl35" bibid="bib32" firstref="ref54"></nolink> <nolink nlid="nl36" bibid="bib78" firstref="ref55"></nolink> <nolink nlid="nl37" bibid="bib35" firstref="ref56"></nolink> <nolink nlid="nl38" bibid="bib77" firstref="ref57"></nolink> <nolink nlid="nl39" bibid="bib53" firstref="ref58"></nolink> <nolink nlid="nl40" bibid="bib54" firstref="ref59"></nolink> <nolink nlid="nl41" bibid="bib59" firstref="ref60"></nolink> <nolink nlid="nl42" bibid="bib73" firstref="ref61"></nolink> <nolink nlid="nl43" bibid="bib19" firstref="ref62"></nolink> <nolink nlid="nl44" bibid="bib22" firstref="ref63"></nolink> <nolink nlid="nl45" bibid="bib68" firstref="ref67"></nolink> <nolink nlid="nl46" bibid="bib33" firstref="ref70"></nolink> <nolink nlid="nl47" bibid="bib51" firstref="ref71"></nolink> <nolink nlid="nl48" bibid="bib63" firstref="ref72"></nolink> <nolink nlid="nl49" bibid="bib48" firstref="ref74"></nolink> <nolink nlid="nl50" bibid="bib39" firstref="ref80"></nolink> <nolink nlid="nl51" bibid="bib56" firstref="ref82"></nolink> <nolink nlid="nl52" bibid="bib38" firstref="ref84"></nolink> <nolink nlid="nl53" bibid="bib29" firstref="ref85"></nolink> <nolink nlid="nl54" bibid="bib16" firstref="ref86"></nolink> <nolink nlid="nl55" bibid="bib17" firstref="ref93"></nolink> <nolink nlid="nl56" bibid="bib49" firstref="ref95"></nolink> <nolink nlid="nl57" bibid="bib55" firstref="ref96"></nolink> <nolink nlid="nl58" bibid="bib37" firstref="ref97"></nolink> <nolink nlid="nl59" bibid="bib52" firstref="ref99"></nolink> <nolink nlid="nl60" bibid="bib12" firstref="ref100"></nolink> <nolink nlid="nl61" bibid="bib28" firstref="ref101"></nolink> <nolink nlid="nl62" bibid="bib24" firstref="ref102"></nolink> <nolink nlid="nl63" bibid="bib61" firstref="ref103"></nolink> <nolink nlid="nl64" bibid="bib30" firstref="ref104"></nolink> <nolink nlid="nl65" bibid="bib69" firstref="ref105"></nolink> <nolink nlid="nl66" bibid="bib25" firstref="ref106"></nolink> <nolink nlid="nl67" bibid="bib14" firstref="ref111"></nolink> <nolink nlid="nl68" bibid="bib72" firstref="ref114"></nolink> <nolink nlid="nl69" bibid="bib18" firstref="ref117"></nolink>
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  Label: Title
  Group: Ti
  Data: Bridging the Digital Divide: The Mediating Role of Learning Engagement between Technology Usage Approaches and Higher Order Thinking Skills in a Technology-Enhanced Inquiry-Based Learning Environment
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kaili+Lu%22">Kaili Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Tongling+Ji%22">Tongling Ji</searchLink><br /><searchLink fieldCode="AR" term="%22Feng+Lu%22">Feng Lu</searchLink><br /><searchLink fieldCode="AR" term="%22Rustam+Shadiev%22">Rustam Shadiev</searchLink> (ORCID <externalLink term="http://orcid.org/0000-0001-5571-1158">0000-0001-5571-1158</externalLink>)
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  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Educational+Technology+Research+and+Development%22"><i>Educational Technology Research and Development</i></searchLink>. 2025 73(5):2929-2949.
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  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: 2025
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Learner+Engagement%22">Learner Engagement</searchLink><br /><searchLink fieldCode="DE" term="%22Access+to+Computers%22">Access to Computers</searchLink><br /><searchLink fieldCode="DE" term="%22Disadvantaged%22">Disadvantaged</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Thinking+Skills%22">Thinking Skills</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+Processes%22">Cognitive Processes</searchLink><br /><searchLink fieldCode="DE" term="%22Inquiry%22">Inquiry</searchLink><br /><searchLink fieldCode="DE" term="%22Mediation+Theory%22">Mediation Theory</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1007/s11423-025-10533-7
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1042-1629<br />1556-6501
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The integration of technology in the learning landscape has precipitated the need to understand its relationship with students' cognitive processes. However, there is a gap in understanding how learning engagement interacts with two approaches to using technology and how these, in turn, impact higher order thinking skills (HOTS). This study aimed to explore the mediating role of learning engagement between approaches to using technology and HOTS within the technology-enhanced inquiry-based learning (T-IBL) framework. Data were collected from a sample of 160 college students experienced in T-IBL environments. Structural equation modeling was used to analyse the relationship between these key variables. The results showed that students' deep approach to using technologies had direct and significant positive impacts on learning engagement and HOTS. While students' surface approach to using technologies had direct negative influences on learning engagement and HOTS, they were not significant. What is more, learning engagement had direct and significant positive impacts on HOTS. In other words, learning engagement act as a mediator between students' deep approach to using technologies and HOTS, but not between surface approach and HOTS. This research fills an existing gap by elucidating the intricate relationship between technology use, engagement, and cognitive processes in a T-IBL setting. The findings underscore the importance of fostering deeper engagement and mindful technology use to enhance HOTS in learners, offering invaluable insights for educators and curriculum developers in the digital age.
– Name: AbstractInfo
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  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2026
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1497657
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1497657
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      – Type: doi
        Value: 10.1007/s11423-025-10533-7
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      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 21
        StartPage: 2929
    Subjects:
      – SubjectFull: Learner Engagement
        Type: general
      – SubjectFull: Access to Computers
        Type: general
      – SubjectFull: Disadvantaged
        Type: general
      – SubjectFull: Technology Uses in Education
        Type: general
      – SubjectFull: Thinking Skills
        Type: general
      – SubjectFull: Cognitive Processes
        Type: general
      – SubjectFull: Inquiry
        Type: general
      – SubjectFull: Mediation Theory
        Type: general
    Titles:
      – TitleFull: Bridging the Digital Divide: The Mediating Role of Learning Engagement between Technology Usage Approaches and Higher Order Thinking Skills in a Technology-Enhanced Inquiry-Based Learning Environment
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            NameFull: Tongling Ji
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            NameFull: Feng Lu
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              Y: 2025
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              Value: 73
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              Value: 5
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            – TitleFull: Educational Technology Research and Development
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