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Factors That Influence Behavioral Intention to Use Mobile-Based Assessment: A STEM Teachers' Perspective

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Title: Factors That Influence Behavioral Intention to Use Mobile-Based Assessment: A STEM Teachers' Perspective
Language: English
Authors: Nikou, Stavros A., Economides, Anastasios A.
Source: British Journal of Educational Technology. Mar 2019 50(2):587-600.
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: 14
Publication Date: 2019
Document Type: Journal Articles
Reports - Research
Descriptors: STEM Education, Telecommunications, Handheld Devices, Educational Technology, Technology Uses in Education, Technology Integration, Teacher Attitudes, Intention, Difficulty Level, Teaching Methods, Foreign Countries
Geographic Terms: Europe
DOI: 10.1111/bjet.12609
ISSN: 0007-1013
Abstract: Teachers' role can be catalytic in the introduction of innovative digital tools in order to create new learning and assessment opportunities. This study explores science technology engineering and mathematics (STEM) teachers' intention to use mobile-based assessments in the teaching practice. The study proposes the teachers' acceptance mobile-based assessment (TAMBA) model which extends the technology acceptance model by introducing individual, social, institutional and instructional design factors. An appropriate questionnaire was developed and answered by 161 STEM teachers from 32 European countries. Their responses were analyzed using structural equation modeling. The proposed TAMBA model explains about 50% of the variance in teachers' intention to adopt mobile-based assessment. Perceived Ease of Use was found to be the most important determinant in teachers' intention to use mobile-based assessment. Facilitating Conditions and Output Quality were the most influential external variables in the model. The study findings revealed that focusing on mobile assessment quality design as well as on institutional support are important factors for STEM teachers in order to accept mobile-based assessments in schools.
Abstractor: As Provided
Entry Date: 2019
Accession Number: EJ1206943
Database: ERIC
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  Value: <anid>AN0134931074;58i01mar.19;2019Feb28.04:47;v2.2.500</anid> <title id="AN0134931074-1">Factors that influence behavioral intention to use mobile‐based assessment: A STEM teachers' perspective </title> <sbt id="AN0134931074-2">Practitioner Notes</sbt> <p>Teachers' role can be catalytic in the introduction of innovative digital tools in order to create new learning and assessment opportunities. This study explores science technology engineering and mathematics (STEM) teachers' intention to use mobile‐based assessments in the teaching practice. The study proposes the teachers' acceptance mobile‐based assessment (TAMBA) model which extends the technology acceptance model by introducing individual, social, institutional and instructional design factors. An appropriate questionnaire was developed and answered by 161 STEM teachers from 32 European countries. Their responses were analyzed using structural equation modeling. The proposed TAMBA model explains about 50% of the variance in teachers' intention to adopt mobile‐based assessment. Perceived Ease of Use was found to be the most important determinant in teachers' intention to use mobile‐based assessment. Facilitating Conditions and Output Quality were the most influential external variables in the model. The study findings revealed that focusing on mobile assessment quality design as well as on institutional support are important factors for STEM teachers in order to accept mobile‐based assessments in schools.</p> <p>What is already known about this topic</p> <p></p> <ulist> <item> Teachers' role can be catalytic in the introduction of innovative digital tools in education in order to create new learning and assessment experiences and opportunities.</item> <p></p> <item> Limited research exists about the factors that influence teachers' acceptance of mobile learning and no study exists that investigates teachers' acceptance of mobile‐based assessment.</item> <p></p> <item> Individual factors, such as ICT and mobile self‐efficacy, are important determinants towards teachers' acceptance of mobile learning.</item> </ulist> <p>What this paper adds</p> <p></p> <ulist> <item> The model introduces, for the first time in a model about the acceptance of technology‐assisted assessment, the variable of Output Quality.</item> <p></p> <item> Perceived Ease of Use is the most influential determinant in teachers' adoption of mobile‐based assessments.</item> <p></p> <item> Beyond individual factors, the study suggests that instructional design, institutional and social factors are also important determinants towards teachers' adoption of mobile‐based assessment.</item> </ulist> <p>Implications for practice and/or policy</p> <p></p> <ulist> <item> Instructional designers and mobile developers should take into their consideration the affordances offered by mobile devices and provide teachers with quality, effective and easily administered mobile‐based assessments.</item> <p></p> <item> Education institutions can better support teachers to integrate mobile‐based assessments in the curriculum, by providing the necessary infrastructure and appropriate attitude development.</item> <p></p> <item> Teacher training programs should provide teachers with the appropriate knowledge and skills necessary to use mobile assessment practices into their teaching.</item> </ulist> <hd id="AN0134931074-3">Introduction</hd> <p>Mobile‐based assessment (MBA), the assessment delivered through wireless technologies and mobile devices, is an emerging trend in the context of Bring Your Own Device movement (Johnson et al., [<reflink idref="bib25" id="ref1">25</reflink>]). Recent studies have shown that mobile technologies have a considerable potential in learning and assessment (Sung, Chang, & Liu, [<reflink idref="bib42" id="ref2">42</reflink>]). However, effective implementation of any information system depends on user's acceptance (Davis, [<reflink idref="bib18" id="ref3">18</reflink>]). Therefore, successful implementation of mobile‐based assessment requires investigation of its adoption from both students' and teachers' perspectives (Kim, Kim, Lee, Spector, & DeMeester, [<reflink idref="bib26" id="ref4">26</reflink>]). Regarding the students' point of view, there are few studies investigating the acceptance of mobile‐based assessment (Nikou & Economides, [<reflink idref="bib33" id="ref5">33</reflink>]). The current study aims to investigate the factors that influence science technology engineering and mathematics (STEM) teachers' behavioral intention to use (BIU) mobile‐based assessment. The study introduces instructional design, individual, institutional and social factors as external variables into the technology acceptance model (TAM) in order to ultimately facilitate MBA adoption from both a bottom‐up and/or top‐down approach (Chiu, [<reflink idref="bib10" id="ref6">10</reflink>]).</p> <hd id="AN0134931074-4">Literature review</hd> <p></p> <hd id="AN0134931074-5">Mobile‐based assessment</hd> <p>There are many successful mobile‐based assessment implementations reported in the related literature: classroom polling (Stowell, [<reflink idref="bib40" id="ref7">40</reflink>]), assessments blended into learning management systems (Bogdanovic, Barac, Jovanic, Popovic, & Radenkovic, [<reflink idref="bib4" id="ref8">4</reflink>]), formative assessments (Hwang & Chang, [<reflink idref="bib24" id="ref9">24</reflink>]), peer‐and self‐assessments (Chen, [<reflink idref="bib6" id="ref10">6</reflink>]), performance‐based assessments (Campbell & Main, [<reflink idref="bib5" id="ref11">5</reflink>]), competency‐based assessments (Coulby, Hennessey, Davie, & Fuller, [<reflink idref="bib17" id="ref12">17</reflink>]) and ubiquitous and context‐aware authentic assessments (Chu, Hwang, Tsai, & Tseng, [<reflink idref="bib12" id="ref13">12</reflink>]). Mobile technologies offer unique affordances in assessment (Nikou & Economides, [<reflink idref="bib32" id="ref14">32</reflink>]). They provide new and enhanced assessment opportunities, eg, authentic assessment experiences and user‐generated content (Cochrane & Narayan, [<reflink idref="bib14" id="ref15">14</reflink>]), offering the potential to assess competences related to real‐world tasks and higher‐level 21st century skills such as problem‐solving, creativity and collaboration (Farrell & Rushby, [<reflink idref="bib21" id="ref16">21</reflink>]). They offer numerous administration benefits, eg, mobile assessments are digital documents that can be easily used anytime and anywhere, reproduced, modified or processed (UNESCO‐IITE, [<reflink idref="bib45" id="ref17">45</reflink>]). They enable positive student experiences with increased learning interest and improved learning outcome (Wu et al., [<reflink idref="bib52" id="ref18">52</reflink>]). Also, they can effectively support teachers into their everyday teaching practices improving their ongoing professional development (West, [<reflink idref="bib51" id="ref19">51</reflink>]).</p> <p>Mobile‐based assessment can be implemented either as an embedded educational activity in a wider context of a mobile learning strategy or it can be administered in its own context as an autonomous educational activity. Therefore, MBA acceptance should be studied in its own context, separately from mobile learning adoption in general.</p> <hd id="AN0134931074-6">Technology acceptance model</hd> <p>The TAM is a well‐established model that addresses the issue of how users accept and use a technology (Davis, [<reflink idref="bib18" id="ref20">18</reflink>]). According to the meta‐analysis by Šumak, Hericko, and Pušnik ([<reflink idref="bib41" id="ref21">41</reflink>]), perceived ease of use (PEOU) and perceived usefulness (PU) are the major factors that influence intention to use e‐learning technologies. TAM is a valid and robust model (King & He, [<reflink idref="bib27" id="ref22">27</reflink>]) and it is the most widely used acceptance model in e‐learning acceptance research (Šumak et al., [<reflink idref="bib41" id="ref23">41</reflink>]). Moreover, it has already been successfully used to explain and predict adoption of mobile learning from students' (Park, Nam, & Cha, [<reflink idref="bib36" id="ref24">36</reflink>]) and teachers' perspectives (Mac Callum, Jeffrey, & Kinshuk, [<reflink idref="bib30" id="ref25">30</reflink>]; Sánchez‐Prieto, Olmos‐Migueláñez, & García‐Peñalvo, [<reflink idref="bib38" id="ref26">38</reflink>]).</p> <hd id="AN0134931074-7">Teachers' acceptance of mobile learning and mobile‐based assessment</hd> <p>Teacher beliefs and attitudes are always critical factors that influence the successful integration of new technologies into classrooms (Kim et al., [<reflink idref="bib26" id="ref27">26</reflink>]; Miranda & Russell, [<reflink idref="bib31" id="ref28">31</reflink>]). Not only are teachers the primary agents that introduce new technologies into the classrooms but they can also influence students' perceptions towards the use of these technologies. Therefore, investigation of the teachers' acceptance of mobile‐based assessment is of utmost importance.</p> <p>Mobile learning and assessment are still new to many schools. Studies reported varying degrees of teachers' willingness to adopt these technologies (Chiu & Churchill, [<reflink idref="bib11" id="ref29">11</reflink>]). There are studies that reported positive teachers' perceptions about the use of mobiles into teaching (Baran, [<reflink idref="bib2" id="ref30">2</reflink>]; Chiu & Churchill, [<reflink idref="bib11" id="ref31">11</reflink>]; Uzunboylu & Özdamli, [<reflink idref="bib46" id="ref32">46</reflink>]). There are also studies that reported that teachers either do not support the use of mobiles in the classroom or they acknowledge uncertainty (Gao, Yan, Wei, Liang, & Mo, [<reflink idref="bib22" id="ref33">22</reflink>]; Thomas, O'Bannon, & Britt, [<reflink idref="bib44" id="ref34">44</reflink>]). Moreover, limited research exists about teachers' acceptance of mobile learning (Cheung & Hew, [<reflink idref="bib7" id="ref35">7</reflink>]).</p> <p>Technology acceptance research, in order to better predict user acceptance, has extended TAM with individual, social and institutional factors (Chiu, [<reflink idref="bib10" id="ref36">10</reflink>]; Tarhini, Hone, & Liu, [<reflink idref="bib43" id="ref37">43</reflink>]). However, based on our review of the literature, in the context of teachers' acceptance of mobile learning, only individual factors (digital literacy, ICT and mobile anxiety and ICT teaching self‐efficacy) have already been tested. These constructs have been found to significantly influence teachers' PEOU and intention to use mobile learning (Mac Callum et al., 2014; Sánchez‐Prieto et al., [<reflink idref="bib38" id="ref38">38</reflink>]). Self‐efficacy is a necessary factor for teacher adoption of new technologies (O'Bannon & Thomas, [<reflink idref="bib35" id="ref39">35</reflink>]). Baydas and Yilmaz ([<reflink idref="bib3" id="ref40">3</reflink>]) proposed a model where teachers' attitudes and cognitive needs have an influence on their behavioral intention while their affective and social needs do not. O'Bannon and Thomas ([<reflink idref="bib35" id="ref41">35</reflink>]) suggested that teachers' perceptions regarding the beneficial mobile features for school related work depends on the teachers' age. To the best of our knowledge, other factors (eg, social, institutional) that may affect teachers' acceptance of mobile learning have not been investigated. Furthermore, no study exists that examines teachers' acceptance of mobile‐based assessment.</p> <hd id="AN0134931074-8">The present study</hd> <p>In order to fill in the gap in existing research, this study proposes and tests the teachers' acceptance mobile‐based assessment (TAMBA) model based on the TAM. The proposed model incorporates the well‐established constructs of PU and PEOU of the TAM, and introduces the following extra constructs: mobile self‐efficacy (MSE) (individual construct), social influence (SI) (social construct), facilitating conditions (FC) (institutional construct) and output quality (OQ) (instructional design construct).</p> <p>Previous studies in different contexts, including mobile learning, found the following relationships regarding these constructs. SI, defined as the "degree to which an individual perceives that important others believe he or she should use the new system," positively affects PU and Intention to Use (Liu, Li, & Carlsson, [<reflink idref="bib28" id="ref42">28</reflink>]; Park et al., [<reflink idref="bib36" id="ref43">36</reflink>]; Venkatesh, Morris, Davis, & Davis, [<reflink idref="bib49" id="ref44">49</reflink>]). FC, defined as "the degree to which users believe that the necessary infrastructures exist to support the use of a technological system," positively affects PU and Intention to Use (El‐Gayar, Moran, & Hawkes, [<reflink idref="bib20" id="ref45">20</reflink>]; Liu et al. [<reflink idref="bib28" id="ref46">28</reflink>]; Venkatesh et al., [<reflink idref="bib49" id="ref47">49</reflink>]; West, [<reflink idref="bib51" id="ref48">51</reflink>]). The current study introduces for the first time in the context of technology‐based assessments the construct of OQ. OQ is defined as "the degree to which an individual believes that the system performs his or her job tasks well" (Venkatesh & Davis, [<reflink idref="bib48" id="ref49">48</reflink>]). In our study about MBA, perceptions of OQ can be considered administration benefits (eg, ease to reuse, modify and grade the mobile assessment) and also enhanced assessment opportunities (eg, authentic assessments, user‐generated context) that MBA can offer. Previous general technology acceptance research has shown that OQ has a positive effect on PU (Venkatesh & Bala, [<reflink idref="bib47" id="ref50">47</reflink>]; Venkatesh & Davis, [<reflink idref="bib48" id="ref51">48</reflink>]).</p> <p>Inline with previous research, our model about teachers' acceptance of mobile‐based assessment proposes the following hypotheses: H1: Perceived Usefulness (PU) has a positive effect on Behavioral Intention to Use (BIU).H2a: Perceived Ease of Use (PEOU) has a positive effect on Perceived Usefulness (PU).H2b: Perceived Ease of Use (PEOU) has a positive effect on Behavioral Intention to Use (BIU).H3: Social Influence (SI) has a positive effect on Perceived Usefulness (PU).H4: Output Quality (OQ) has a positive effect on Perceived Usefulness (PU).H5. Mobile Self‐Efficacy (MSE) has a positive effect on Perceived Ease of Use (PEU).H6: Facilitating Conditions (FC) have a positive effect on Perceived Ease of Use (PEOU).</p> <p>Previous research on learning technologies adoption, suggested that social, institutional and individual factors may facilitate a top–down adoption approach (eg, educational policy oblige teachers to use the technology) while instructional design factors facilitate a bottom‐up approach (eg, teachers voluntarily use the technology) (Chiu, [<reflink idref="bib10" id="ref52">10</reflink>]). Therefore, the study findings will provide suggestions on how to amplify the adoption of mobile‐based assessments from teachers with a top–down and/or a bottom‐up approach.</p> <hd id="AN0134931074-9">Methodology</hd> <p></p> <hd id="AN0134931074-10">Participants and procedure</hd> <p>A total of 215 STEM teachers were randomly contacted to participate in the survey. A total of 161 teachers completed the survey (response rate 74.8%). The participants were from 32 different European countries, as Table 1 shows. The basic inclusion criteria for our study were the participants' teaching major to be related to STEM subjects. All participating teachers were active members of science education communities with experience in technology‐supported approaches to science and mathematics education and high engagement in science education EU funded projects. Teachers were contacted either through their personal emails or through education related social media channels. Teachers were informed in advance about the research procedure and were asked to fill‐in an online survey. Participation was voluntarily and all the data collected anonymously.</p> <p>Countries of the participating teachers (n = 161)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Country</th><th align="center">f</th><th align="center">Country</th><th align="center">f</th><th align="center">Country</th><th align="center">f</th><th align="center">Country</th><th align="center">f</th></tr></thead><tbody valign="top"><tr><td align="left">Austria</td><td align="center">2</td><td align="left">Finland</td><td align="char" char=".">4</td><td align="left">Israel</td><td align="char" char=".">2</td><td align="left">Portugal</td><td align="char" char=".">9</td></tr><tr><td align="left">Belgium</td><td align="center">4</td><td align="left">France</td><td align="char" char=".">4</td><td align="left">Italy</td><td align="char" char=".">9</td><td align="left">Romania</td><td align="char" char=".">10</td></tr><tr><td align="left">Bulgaria</td><td align="center">7</td><td align="left">FYROM</td><td align="char" char=".">2</td><td align="left">Latvia</td><td align="char" char=".">2</td><td align="left">Slovakia</td><td align="char" char=".">3</td></tr><tr><td align="left">Croatia</td><td align="center">3</td><td align="left">Germany</td><td align="char" char=".">3</td><td align="left">Lithuania</td><td align="char" char=".">3</td><td align="left">Slovenia</td><td align="char" char=".">3</td></tr><tr><td align="left">Cyprus</td><td align="center">8</td><td align="left">Greece</td><td align="char" char=".">14</td><td align="left">Malta</td><td align="char" char=".">2</td><td align="left">Spain</td><td align="char" char=".">12</td></tr><tr><td align="left">Czech Reb.</td><td align="center">5</td><td align="left">Hungary</td><td align="char" char=".">5</td><td align="left">Netherlands</td><td align="char" char=".">3</td><td align="left">Sweden</td><td align="char" char=".">4</td></tr><tr><td align="left">Denmark</td><td align="center">2</td><td align="left">Iceland</td><td align="char" char=".">3</td><td align="left">Norway</td><td align="char" char=".">3</td><td align="left">Turkey</td><td align="char" char=".">7</td></tr><tr><td align="left">Estonia</td><td align="center">3</td><td align="left">Ireland</td><td align="char" char=".">4</td><td align="left">Poland</td><td align="char" char=".">10</td><td align="left">UK</td><td align="char" char=".">6</td></tr></tbody></table> </ephtml> </p> <p>Demographical information for the sample group is presented in Table 2. There were 73 males (45.3%) and 88 females (54.6%). Almost half of the teachers (52.17%) were in the age range of 35–50. The majority of the teachers (66.45%) had a quite extensive science teaching experience (more than 10 years). The participating teachers taught the following subjects: Mathematics (11%), Physics (14%), Chemistry (9%), Biology (10%), ICT (11%), Technology (3%) and their combinations. For example, Physics and Chemistry (15%), Physics, Maths and ICT (25%), other (2%). Only 31.67% of the participants had already used mobiles in their classes and 18% of them had used mobile devices for assessment purposes (mostly as clickers for classroom polling).</p> <p>Demographical information of the sample group (n = 161)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center"><italic>f</italic></th><th align="center"><italic>%</italic></th></tr></thead><tbody valign="top"><tr><td align="left">Gender</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">Male</td><td align="char" char=".">73</td><td align="char" char=".">45.30</td></tr><tr><td align="left">Female</td><td align="char" char=".">88</td><td align="char" char=".">54.60</td></tr><tr><td align="left">Age range (in years)</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">20–35</td><td align="char" char=".">42</td><td align="char" char=".">26.08</td></tr><tr><td align="left">35–50</td><td align="char" char=".">84</td><td align="char" char=".">52.17</td></tr><tr><td align="left">50–65</td><td align="char" char=".">35</td><td align="char" char=".">21.70</td></tr><tr><td align="left">School</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">Primary</td><td align="char" char=".">45</td><td align="char" char=".">27.9</td></tr><tr><td align="left">Secondary</td><td align="char" char=".">116</td><td align="char" char=".">72.10</td></tr><tr><td align="left">Teaching experience (in years)</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">Less than 5</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">5–10</td><td align="char" char=".">54</td><td align="char" char=".">33.54</td></tr><tr><td align="left">10–20</td><td align="char" char=".">89</td><td align="char" char=".">55.27</td></tr><tr><td align="left">Over 20</td><td align="char" char=".">18</td><td align="char" char=".">11.18</td></tr><tr><td align="left">Used mobiles in courses</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">Yes</td><td align="char" char=".">51</td><td align="char" char=".">31.67</td></tr><tr><td align="left">No</td><td align="char" char=".">110</td><td align="char" char=".">68.33</td></tr><tr><td align="left">Mobiles experience period (in years)</td><td align="char" char="." /><td align="char" char="." /></tr><tr><td align="left">0–2</td><td align="char" char=".">15</td><td align="char" char=".">9.32</td></tr><tr><td align="left">2–5</td><td align="char" char=".">32</td><td align="char" char=".">19.87</td></tr><tr><td align="left">5 or more</td><td align="char" char=".">114</td><td align="char" char=".">70.81</td></tr></tbody></table> </ephtml> </p> <hd id="AN0134931074-11">Instruments</hd> <p>In order to develop the instrument for our research about the acceptance of mobile‐based assessment, we have adapted some items of the constructs from previously validated instruments. For PU, PEOU, BIU, SI, FC, we have adopted items from Venkatesh et al. ([<reflink idref="bib49" id="ref53">49</reflink>]). For MSE, an individual's perceptions of his or her ability to use mobile devices to accomplish particular tasks, we adapted items from Compeau and Higgins ([<reflink idref="bib16" id="ref54">16</reflink>]), properly modified for the context of mobile‐based assessment. For the OQ, based on items from Venkatesh and Davis ([<reflink idref="bib48" id="ref55">48</reflink>]), we developed four items properly modified and adapted to reflect enhanced assessment opportunities and administration benefits of mobile‐based assessments. Cronbach's a for all constructs were above 0.70, ensuring the internal consistency of the instrument.</p> <p>Our measurement instrument consists of 7 constructs making a total of 25 items. The questionnaire was developed and distributed in English. All items were measured on a 7‐point Likert‐type scale with 1 corresponding to "strongly disagree" and 7 to "strongly agree." The questionnaire used is shown in Figure S1 in Appendix (as Supporting Information).</p> <hd id="AN0134931074-12">Data analysis and results</hd> <p>The analysis technique used to predict the factors affecting mobile‐based assessment adoption from the teachers' perspective was partial least‐squares (PLS) with Smart PLS 2.0 (Ringle, Wende, & Will, [<reflink idref="bib37" id="ref56">37</reflink>]). Our sample size exceeds the recommended value of 40, ie, 10 times larger than the number of items for the most complex construct (Chin, [<reflink idref="bib9" id="ref57">9</reflink>]).</p> <hd id="AN0134931074-13">Instrument validation</hd> <p>Internal consistency, convergent and discriminant validity of the proposed research model need to be verified to ensure its quality. All criteria for convergent validity are satisfied: all factor loadings on their relative construct exceed 0.70, composite reliability of each construct exceed 0.70 and all average variance extracted (AVE) values range from 0.594 to 0.773 (AVE > 0.50) exceeding the variance due to measurement error for that construct (Table 3). Discriminant validity is also supported since the square root of the AVE of a construct is higher than any correlation with another construct (Table 4). Thus, both convergent and discriminant validity for the proposed research model are verified.</p> <p>Descriptive statistics and results for convergent validity for the measurement model (acceptable threshold values in brackets)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Construct items</th><th align="center">Mean (SD)</th><th align="center">Factor loading (>0.70)</th><th align="center">Cronbach's a (>0.70)</th><th align="center">Composite reliability (>0.70)</th><th align="center">Average variance extracted (>0.50)</th></tr></thead><tbody valign="top"><tr><td align="left">Perceived ease of use</td><td align="char" char=".">5.09 (1.28)</td><td align="left" /><td align="char" char=".">0.851</td><td align="char" char=".">0.910</td><td align="char" char=".">0.773</td></tr><tr><td align="left">PEOU1</td><td align="left" /><td align="char" char=".">0.811</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">PEOU2</td><td align="left" /><td align="char" char=".">0.864</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">PEOU3</td><td align="left" /><td align="char" char=".">0.957</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Perceived usefulness</td><td align="char" char=".">3.95 (1.26)</td><td align="left" /><td align="char" char=".">0.730</td><td align="char" char=".">0.845</td><td align="char" char=".">0.647</td></tr><tr><td align="left">PU1</td><td align="left" /><td align="char" char=".">0.801</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">PU2</td><td align="left" /><td align="char" char=".">0.753</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">PU3</td><td align="left" /><td align="char" char=".">0.857</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Social influence</td><td align="char" char=".">4.95 (1.25)</td><td align="left" /><td align="char" char=".">0.799</td><td align="char" char=".">0.859</td><td align="char" char=".">0.604</td></tr><tr><td align="left">SI1</td><td align="left" /><td align="char" char=".">0.731</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">SI2</td><td align="left" /><td align="char" char=".">0.836</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">SI3</td><td align="left" /><td align="char" char=".">0.808</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">SI4</td><td align="left" /><td align="char" char=".">0.730</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Facilitating conditions</td><td align="char" char=".">4.96 (1.18)</td><td align="left" /><td align="char" char=".">0.819</td><td align="char" char=".">0.880</td><td align="char" char=".">0.648</td></tr><tr><td align="left">FC1</td><td align="left" /><td align="char" char=".">0.813</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">FC2</td><td align="left" /><td align="char" char=".">0.802</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">FC3</td><td align="left" /><td align="char" char=".">0.822</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">FC4</td><td align="left" /><td align="char" char=".">0.783</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Output quality</td><td align="char" char=".">4.63 (1.20)</td><td align="left" /><td align="char" char=".">0.827</td><td align="char" char=".">0.884</td><td align="char" char=".">0.657</td></tr><tr><td align="left">OQ1</td><td align="left" /><td align="char" char=".">0.829</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">OQ2</td><td align="left" /><td align="char" char=".">0.836</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">OQ3</td><td align="left" /><td align="char" char=".">0.784</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">OQ4</td><td align="left" /><td align="char" char=".">0.798</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Mobile self‐efficacy</td><td align="char" char=".">3.78 (1.19)</td><td align="left" /><td align="char" char=".">0.774</td><td align="char" char=".">0.853</td><td align="char" char=".">0.594</td></tr><tr><td align="left">MSE1</td><td align="left" /><td align="char" char=".">0.776</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">MSE2</td><td align="left" /><td align="char" char=".">0.737</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">MSE3</td><td align="left" /><td align="char" char=".">0.827</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">MSE4</td><td align="left" /><td align="char" char=".">0.738</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">Behavioral intention to use</td><td align="char" char=".">4.80 (1.25)</td><td align="left" /><td align="char" char=".">0.724</td><td align="char" char=".">0.844</td><td align="char" char=".">0.645</td></tr><tr><td align="left">BIU1</td><td align="left" /><td align="char" char=".">0.811</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">BIU2</td><td align="left" /><td align="char" char=".">0.812</td><td align="left" /><td align="left" /><td align="left" /></tr><tr><td align="left">BIU3</td><td align="left" /><td align="char" char=".">0.787</td><td align="left" /><td align="left" /><td align="left" /></tr></tbody></table> </ephtml> </p> <p>Discriminant validity for the measurement model (values in bold: the square root of the AVE for each construct)</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left" /><th align="center">BIU</th><th align="center">FC</th><th align="center">MSE</th><th align="center">OQ</th><th align="center">PEOU</th><th align="center">PU</th><th align="center">SI</th></tr></thead><tbody valign="top"><tr><td align="left">BIU</td><td align="center"><bold>0.80</bold></td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">FC</td><td align="center">0.79</td><td align="center"><bold>0.81</bold></td><td align="center" /><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">MSE</td><td align="center">0.60</td><td align="center">0.61</td><td align="center"><bold>0.77</bold></td><td align="center" /><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">OQ</td><td align="center">0.64</td><td align="center">0.74</td><td align="center">0.65</td><td align="center"><bold>0.81</bold></td><td align="center" /><td align="center" /><td align="center" /></tr><tr><td align="left">PEOU</td><td align="center">0.65</td><td align="center">0.65</td><td align="center">0.58</td><td align="center">0.65</td><td align="center"><bold>0.87</bold></td><td align="center" /><td align="center" /></tr><tr><td align="left">PU</td><td align="center">0.60</td><td align="center">0.62</td><td align="center">0.57</td><td align="center">0.65</td><td align="center">0.57</td><td align="center"><bold>0.80</bold></td><td align="center" /></tr><tr><td align="left">SI</td><td align="center">0.52</td><td align="center">0.51</td><td align="center">0.65</td><td align="center">0.55</td><td align="center">0.45</td><td align="center">0.54</td><td align="center"><bold>0.77</bold></td></tr></tbody></table> </ephtml> </p> <p>126090001 BIU, behavioral intention to use; PEOU, perceived ease of use; PU, perceived usefulness; SI, social influence; FC, facilitating conditions; OQ, output quality; MSE, mobile self‐efficacy.</p> <hd id="AN0134931074-14">Test of the structured model and hypotheses</hd> <p>Figure 1 and Table 5 summarize the structural model and the hypothesis testing results. Figure 1 shows the path coefficient for each path along with its significance (as asterisks, **<emph>p</emph> < .05, ***<emph>p</emph> < .01) and the <emph>R</emph><sups>2</sups> for each endogenous variable. Table 5 shows the statistical significance of the relations in the model. Table 6 shows path analysis result of the direct, indirect and total effects.</p> <p>Graph: Structured Equation Modeling analysis of the proposed TAMBA model [Colour figure can be viewed at ]</p> <p>Graph: image_n/bjet12609-fig-0001.png</p> <p>Hypothesis testing results</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Hypothesis</th><th align="center">Path</th><th align="center">Path coefficient</th><th align="center">t Value</th><th align="center">Results</th></tr></thead><tbody valign="top"><tr><td align="left">H1</td><td align="left">PU → BIU</td><td align="char" char=".">0.332<xref ref-type="fn" rid="bjet12609-note-0002" /></td><td align="char" char=".">8.289</td><td align="left">Support</td></tr><tr><td align="left">H2a</td><td align="left">PEOU → PU</td><td align="char" char=".">0.225<xref ref-type="fn" rid="bjet12609-note-0002" /></td><td align="char" char=".">4.907</td><td align="left">Support</td></tr><tr><td align="left">H2b</td><td align="left">PEOU → BIU</td><td align="char" char=".">0.465<xref ref-type="fn" rid="bjet12609-note-0002" /></td><td align="char" char=".">10.152</td><td align="left">Support</td></tr><tr><td align="left">H3</td><td align="left">SI → PU</td><td align="char" char=".">0.240<xref ref-type="fn" rid="bjet12609-note-0002" /></td><td align="char" char=".">6.771</td><td align="left">Support</td></tr><tr><td align="left">H4</td><td align="left">OQ→ PU</td><td align="char" char=".">0.370<xref ref-type="fn" rid="bjet12609-note-0002" /></td><td align="char" char=".">7.660</td><td align="left">Support</td></tr><tr><td align="left">H5</td><td align="left">MSE → PEOU</td><td align="char" char=".">0.300<xref ref-type="fn" rid="bjet12609-note-0002" /></td><td align="char" char=".">6.608</td><td align="left">Support</td></tr><tr><td align="left">H6</td><td align="left">FC → PEOU</td><td align="char" char=".">0.468<xref ref-type="fn" rid="bjet12609-note-0002" /></td><td align="char" char=".">9.334</td><td align="left">Support</td></tr></tbody></table> </ephtml> </p> <p>126090002 **<emph>p</emph> < .05, ***<emph>p</emph> < .01.</p> <p>R 2 , direct, indirect and total effects</p> <p> <ephtml> <table><thead valign="bottom"><tr><th align="left">Endogenous variable</th><th align="center">Erogenous variable</th><th align="center">Direct effect</th><th align="center">Indirect effect</th><th align="center">Total effect</th><th align="center">R<sup>2</sup></th><th align="center">Results</th></tr></thead><tbody valign="top"><tr><td align="left">Perceived ease of use</td><td align="left">Mobile self‐efficacy</td><td align="char" char=".">0.300</td><td align="center">–</td><td align="char" char=".">0.300</td><td align="char" char=".">0.480</td><td align="left">Support<xref ref-type="fn" rid="bjet12609-note-0003" /></td></tr><tr><td align="left" /><td align="left">Facilitating conditions</td><td align="char" char=".">0.468</td><td align="center">–</td><td align="char" char=".">0.468</td><td align="left" /><td align="left">Support<xref ref-type="fn" rid="bjet12609-note-0003" /></td></tr><tr><td align="left">Perceived usefulness</td><td align="left">Social influence</td><td align="char" char=".">0.240</td><td align="center">–</td><td align="char" char=".">0.240</td><td align="char" char=".">0.503</td><td align="left">Support<xref ref-type="fn" rid="bjet12609-note-0003" /></td></tr><tr><td align="left" /><td align="left">Output quality</td><td align="char" char=".">0.370</td><td align="center">–</td><td align="char" char=".">0.370</td><td align="left" /><td align="left">Support<xref ref-type="fn" rid="bjet12609-note-0003" /></td></tr><tr><td align="left" /><td align="left">Mobile self‐efficacy</td><td align="center">–</td><td align="char" char=".">0.068</td><td align="char" char=".">0.068</td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left">Facilitating conditions</td><td align="center">–</td><td align="char" char=".">0.105</td><td align="char" char=".">0.105</td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left">Perceived ease of use</td><td align="char" char=".">0.225</td><td align="center">–</td><td align="char" char=".">0.225</td><td align="left" /><td align="left">Support<xref ref-type="fn" rid="bjet12609-note-0003" /></td></tr><tr><td align="left">Behavioral intention to use</td><td align="left">Social influence</td><td align="center">–</td><td align="char" char=".">0.080</td><td align="char" char=".">0.080</td><td align="char" char=".">0.504</td><td align="left" /></tr><tr><td align="left" /><td align="left">Output quality</td><td align="center">–</td><td align="char" char=".">0.123</td><td align="char" char=".">0.123</td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left">Mobile self‐efficacy</td><td align="center">–</td><td align="char" char=".">0.162</td><td align="char" char=".">0.162</td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left">Facilitating conditions</td><td align="center">–</td><td align="char" char=".">0.252</td><td align="char" char=".">0.252</td><td align="left" /><td align="left" /></tr><tr><td align="left" /><td align="left">Perceived ease of use</td><td align="char" char=".">0.465</td><td align="char" char=".">0.075</td><td align="char" char=".">0.540</td><td align="left" /><td align="left">Support<xref ref-type="fn" rid="bjet12609-note-0003" /></td></tr><tr><td align="left" /><td align="left">Perceived usefulness</td><td align="char" char=".">0.332</td><td align="center">–</td><td align="char" char=".">0.332</td><td align="left" /><td align="left">Support<xref ref-type="fn" rid="bjet12609-note-0003" /></td></tr></tbody></table> </ephtml> </p> <p>126090003 **<emph>p</emph> < .05, ***<emph>p</emph> < .01.</p> <p>The results from the PLS analysis support all seven hypotheses. The proposed TAMBA model explains and predicts about 50% of the teachers' BIU MBA. This is an acceptable value since it is in the range of 22–68% that is usually found in the mobile learning acceptance research (Baydas & Yilmaz, [<reflink idref="bib3" id="ref58">3</reflink>]). Three endogenous variables— PEOU, PU and BIU—were tested. All four exogenous variables—SI, OQ, FC and MSE—had a positive effect on endogenous variables. Considering the direct effects, all standardized path coefficients have values between 0.240 and 0.468. These values are considered to be medium to large (Cohen, [<reflink idref="bib15" id="ref59">15</reflink>]).</p> <p>Two exogenous variables explained 48% of the variance in PEOU. FC had the largest effect on PEOU (0.468), followed by MSE (0.300). Five variables explained 50% of the variance in PU. OQ had the largest effect on PU (0.370), followed by SI (0.240), PEOU (0.225), FC (0.105) and MSE (0.068). Moreover, seven variables explained 50% of the variance in BIU. The aforementioned value is comparable with other TAM related studies about mobile learning adoption (Wang, Wu, & Wang, [<reflink idref="bib50" id="ref60">50</reflink>]). PEOU had the largest effect on BIU (0.540) followed by PU (0.332), FC (0.252), MSE (0.162), OQ (0.123) and SI (0.080). Among the four external variables, FC had the strongest influence on Behavior Intention to Use following by OQ and MSE.</p> <hd id="AN0134931074-15">Discussions and conclusions</hd> <p>Teachers' role can be catalytic in the acceptance of mobile learning and assessment (Chiu & Churchill, [<reflink idref="bib11" id="ref61">11</reflink>]). While previous studies have focused on teachers' acceptance of mobile learning (Mac Callum et al., 2014; Sánchez‐Prieto et al., [<reflink idref="bib38" id="ref62">38</reflink>]), the current study provides a step ahead towards the investigation of European STEM teachers' acceptance of mobile‐based assessment using individual, social, institutional and instruction design factors and proposes the TAMBA model.</p> <p>The study provided evidence that PEOU is the most dominant determinant of BIU mobile‐based assessment with the PU to follow. PEOU also positively influenced PU. The results are in line with previous research on technology acceptance (Venkatesh et al., [<reflink idref="bib49" id="ref63">49</reflink>]), students' mobile learning and assessment acceptance (Chung, Chen, & Kuo, [<reflink idref="bib13" id="ref64">13</reflink>]; Nikou & Economides, [<reflink idref="bib33" id="ref65">33</reflink>]) and also teachers' acceptance of mobile‐learning (Mac Callum et al., 2014; Sánchez‐Prieto et al., [<reflink idref="bib38" id="ref66">38</reflink>]). Teachers are more likely to assign mobile‐based assessments to their students if they perceive this type of assessment as easy to use and also useful for their teaching.</p> <p>To the best of our knowledge, OQ has been introduced for the first time in the context of technology‐based assessment acceptance research. OQ has been attributed to the administration benefits and also to the potential enhanced assessment opportunities that MBA can offer. OQ, as an instructional design factor, had a significant direct positive effect on PU. It also has a strong indirect effect on BIU through PU. The findings are in line with previous general technology acceptance research (Venkatesh & Bala, [<reflink idref="bib47" id="ref67">47</reflink>]; Venkatesh & Davis, [<reflink idref="bib48" id="ref68">48</reflink>]). Most teachers acknowledge the benefits of using technology‐based assessments for students learning and perceive technology‐based assessments as useful tools (Chien, Wu, & Hsu, [<reflink idref="bib8" id="ref69">8</reflink>]). Based on our results, when a mobile‐based assessment is properly designed to enhance assessment opportunities and facilitate at the same time exam administration offering high quality output it is more likely to be considered useful and, therefore, to be adopted by the teachers.</p> <p>SI, as a social factor, had a positive effect on PU. While SI has already been identified as a significant factor that affects students' PU and students' Intention to Use mobile learning (Park et al., [<reflink idref="bib36" id="ref70">36</reflink>]; Wang et al., [<reflink idref="bib50" id="ref71">50</reflink>]) and mobile‐based assessment (Nikou & Economides, [<reflink idref="bib33" id="ref72">33</reflink>]), studies on teachers' acceptance of mobile learning have provided contradictory results. Our findings imply that when teachers know that their school management, colleagues and educational authorities approve of and value the use of mobile devices in assessment, then they perceive it as a useful educational activity. However, other studies suggested that SI does not affect PU of mobile learning (Baydas & Yilmaz, [<reflink idref="bib3" id="ref73">3</reflink>]). Also, according to Arpaci ([<reflink idref="bib1" id="ref74">1</reflink>]), investigation of the impact of SI on Intention to Use should also consider the mediating effect of cultural factors.</p> <p>Considering the effects on PU, OQ had a greater impact than SI. This result indicates that teachers' needs for useful and better quality mobile enhanced assessments are stronger than their social normative beliefs.</p> <p>MSE, as an individual factor, had a significant direct positive effect on PEOU and an indirect effect on BIU through PEOU. This is in line with previous findings. Research has shown that ICT (Information and Communication Technologies) self‐efficacy enhance ICT teachers' adoption (Scherer, Siddiq, & Teo, [<reflink idref="bib39" id="ref75">39</reflink>]). Moreover, computer self‐efficacy significantly affects the adoption of computer‐based testing (Lu, Hu, Gao, & Kinshuk, [<reflink idref="bib29" id="ref76">29</reflink>]). Also, basic ICT literacy and specific mobile literacy significantly affect teachers' adoption of mobile learning (Mac Callum et al., 2014). Furthermore, after using mobile devices, STEM teachers reported better attitudes, beliefs and acceptance of mobile technologies in learning, while this was not true for Language and Humanities teachers (Chiu & Churchill, [<reflink idref="bib11" id="ref77">11</reflink>]).</p> <p>FC, as an institutional factor, had a significant direct positive effect on PEOU and an indirect effect on BIU through PEOU. The appropriate technical and administrative infrastructure enhances teachers' perceptions of the Ease of Use. Moreover, given the effects on PEOU, our results show that FC had a greater effect than MSE. The above findings are inline with technology acceptance (Davis, [<reflink idref="bib18" id="ref78">18</reflink>]; Venkatesh et al., [<reflink idref="bib49" id="ref79">49</reflink>]), teachers' acceptance of mobile learning (Sánchez‐Prieto et al., [<reflink idref="bib38" id="ref80">38</reflink>]) and students' acceptance of mobile‐based assessments (Nikou & Economides, [<reflink idref="bib33" id="ref81">33</reflink>]) as well.</p> <p>Based on our findings, the following suggestions can be made to mobile developers and instructional designers, education administrators (eg, school directors), teacher training institutions and teachers. Mobile developers and instructional designers should employ into mobile‐based assessments the appropriate quality characteristics that can positively affect teachers' acceptance. Examples include assessments allowing two‐way communication in real time facilitating teacher feedback and peer collaboration, assessments creating new opportunities for student‐generated content and personalized assessments adapting to students' contexts and providing teachers with deeper assessment opportunities. Instructional designers and mobile developers should also harness the capabilities offered by mobiles for assessments that are easily delivered anywhere and anytime, easily reused, modified and graded. The aforementioned design characteristics can facilitate the bottom‐up adoption process (Chiu, [<reflink idref="bib10" id="ref82">10</reflink>]).</p> <p>Education administration (eg, school policies and principals) should provide teachers with all the resources needed and the proper management, institutional and technical infrastructure and support in order to support and facilitate them into the use of mobiles in assessment practices. Education authorities, school counselors and teacher mentors should encourage teachers to integrate mobile assessment practices in the curriculum. Innovative practices such as mobile‐based formative assessments with immediate or adaptive feedback, peer‐assessment and ubiquitous context‐aware assessments have the potential to facilitate the assessment of the 21st century skills including critical thinking, communication, collaboration and creating content (West, [<reflink idref="bib51" id="ref83">51</reflink>]). Even when teachers are not efficacious enough in mobile assessment practices, they are more likely to use mobile‐based assessment and also involve their students into this, when the appropriate FC are present.</p> <p>In‐service and pre‐service teacher education and training institutions should face the challenge of providing teachers the knowledge and skills necessary to use mobile technologies into their teaching and assessment. Teachers should be informed about the affordances offered by mobile technologies in order to advance their own teaching and assessment methodologies. Teacher continuous professional development programs should train teachers into new and innovative assessment methods in order to prepare them to effectively assess 21st century learners. Enhancing teachers' efficacy into using mobiles into their teaching can accelerate the adoption of mobile‐based assessment. The support offered by educational administration and teacher training institutions facilitate the top–down adoption process (Chiu, [<reflink idref="bib10" id="ref84">10</reflink>]).</p> <p>Also, there is an issue connecting to STEM education that we would like to discuss. Previous research has shown that effective technology integration into STEM teaching promotes teacher professional development and also has a positive effect on student learning outcomes (Elliot & Mikulas, [<reflink idref="bib19" id="ref85">19</reflink>]). Therefore, considering the growing popularity of mobile devices and the spreading of BYOD policies, supporting K‐12 STEM teachers to adopt mobile technologies in their classes can be beneficial to the education community (Hu & Garimella, [<reflink idref="bib23" id="ref86">23</reflink>]).</p> <p>One limitation of the current study is that it focuses on STEM teachers. Previous research has shown that Science and Mathematics teachers, compared to Language and Humanities teachers, have more positive attitudes towards mobile devices (Chiu & Churchill, [<reflink idref="bib11" id="ref87">11</reflink>]). Therefore, generalization of the study findings to teacher groups with different backgrounds should be made with caution and future studies should include teachers with other backgrounds as well. Another study limitation stems from the fact that the participants of the study are European teachers. There are cultural differences among European countries and these differences should be reflected in future studies (Nistor, Gogus, & Lerche, [<reflink idref="bib34" id="ref88">34</reflink>]). Moreover, generalization to non‐European contexts should also take into consideration the cultural factors. It would be interesting also to investigate the relation between MBA adoption and other factors eg, gender, age, teaching experience and different assessments types (eg, formative, summative). Moreover, future studies should include other external variables eg, ubiquity, autonomy, security, personalization, enjoyment, satisfaction.</p> <p>This study is the first that investigates teachers' acceptance of mobile‐based assessment considering individual, institutional, social and instructional design factors. The application of mobile technologies into assessment can be a major driver for change throughout teaching and learning (Farrell & Rushby, [<reflink idref="bib21" id="ref89">21</reflink>]) and the teachers' role towards this direction can be a catalyst.</p> <hd id="AN0134931074-16">Statement 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> <p>Graph: Fig S1. Questionnaire Items used</p> <ref id="AN0134931074-17"> <title> References </title> <blist> <bibl id="bib1" idref="ref74" type="bt">1</bibl> <bibtext> Arpaci, I. (2015). A comparative study of the effects of cultural differences on the adoption of mobile learning. 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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. He has published over two hundred peer‐reviewed papers and received over 3000 citations. He is an IEEE Senior member..</p> </aug> <nolink nlid="nl1" bibid="bib25" firstref="ref1"></nolink> <nolink nlid="nl2" bibid="bib42" firstref="ref2"></nolink> <nolink nlid="nl3" bibid="bib18" firstref="ref3"></nolink> <nolink nlid="nl4" bibid="bib26" firstref="ref4"></nolink> <nolink nlid="nl5" bibid="bib33" firstref="ref5"></nolink> <nolink nlid="nl6" bibid="bib10" firstref="ref6"></nolink> <nolink nlid="nl7" bibid="bib40" firstref="ref7"></nolink> <nolink nlid="nl8" bibid="bib24" firstref="ref9"></nolink> <nolink nlid="nl9" bibid="bib17" firstref="ref12"></nolink> <nolink nlid="nl10" bibid="bib12" firstref="ref13"></nolink> <nolink nlid="nl11" bibid="bib32" firstref="ref14"></nolink> <nolink nlid="nl12" bibid="bib14" firstref="ref15"></nolink> <nolink nlid="nl13" bibid="bib21" firstref="ref16"></nolink> <nolink nlid="nl14" bibid="bib45" firstref="ref17"></nolink> <nolink nlid="nl15" bibid="bib52" firstref="ref18"></nolink> <nolink nlid="nl16" bibid="bib51" firstref="ref19"></nolink> <nolink nlid="nl17" bibid="bib41" firstref="ref21"></nolink> <nolink nlid="nl18" bibid="bib27" firstref="ref22"></nolink> <nolink nlid="nl19" bibid="bib36" firstref="ref24"></nolink> <nolink nlid="nl20" bibid="bib30" firstref="ref25"></nolink> <nolink nlid="nl21" bibid="bib38" firstref="ref26"></nolink> <nolink nlid="nl22" bibid="bib31" firstref="ref28"></nolink> <nolink nlid="nl23" bibid="bib11" firstref="ref29"></nolink> <nolink nlid="nl24" bibid="bib46" firstref="ref32"></nolink> <nolink nlid="nl25" bibid="bib22" firstref="ref33"></nolink> <nolink nlid="nl26" bibid="bib44" firstref="ref34"></nolink> <nolink nlid="nl27" bibid="bib43" firstref="ref37"></nolink> <nolink nlid="nl28" bibid="bib35" firstref="ref39"></nolink> <nolink nlid="nl29" bibid="bib28" firstref="ref42"></nolink> <nolink nlid="nl30" bibid="bib49" firstref="ref44"></nolink> <nolink nlid="nl31" bibid="bib20" firstref="ref45"></nolink> <nolink nlid="nl32" bibid="bib48" firstref="ref49"></nolink> <nolink nlid="nl33" bibid="bib47" firstref="ref50"></nolink> <nolink nlid="nl34" bibid="bib16" firstref="ref54"></nolink> <nolink nlid="nl35" bibid="bib37" firstref="ref56"></nolink> <nolink nlid="nl36" bibid="bib15" firstref="ref59"></nolink> <nolink nlid="nl37" bibid="bib50" firstref="ref60"></nolink> <nolink nlid="nl38" bibid="bib13" firstref="ref64"></nolink> <nolink nlid="nl39" bibid="bib39" firstref="ref75"></nolink> <nolink nlid="nl40" bibid="bib29" firstref="ref76"></nolink> <nolink nlid="nl41" bibid="bib19" firstref="ref85"></nolink> <nolink nlid="nl42" bibid="bib23" firstref="ref86"></nolink> <nolink nlid="nl43" bibid="bib34" firstref="ref88"></nolink>
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  Data: Factors That Influence Behavioral Intention to Use Mobile-Based Assessment: A STEM Teachers' Perspective
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  Data: <searchLink fieldCode="AR" term="%22Nikou%2C+Stavros+A%2E%22">Nikou, Stavros A.</searchLink><br /><searchLink fieldCode="AR" term="%22Economides%2C+Anastasios+A%2E%22">Economides, Anastasios A.</searchLink>
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  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
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  Data: <searchLink fieldCode="DE" term="%22STEM+Education%22">STEM Education</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunications%22">Telecommunications</searchLink><br /><searchLink fieldCode="DE" term="%22Handheld+Devices%22">Handheld Devices</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Uses+in+Education%22">Technology Uses in Education</searchLink><br /><searchLink fieldCode="DE" term="%22Technology+Integration%22">Technology Integration</searchLink><br /><searchLink fieldCode="DE" term="%22Teacher+Attitudes%22">Teacher Attitudes</searchLink><br /><searchLink fieldCode="DE" term="%22Intention%22">Intention</searchLink><br /><searchLink fieldCode="DE" term="%22Difficulty+Level%22">Difficulty Level</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Foreign+Countries%22">Foreign Countries</searchLink>
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  Data: 10.1111/bjet.12609
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  Data: Teachers' role can be catalytic in the introduction of innovative digital tools in order to create new learning and assessment opportunities. This study explores science technology engineering and mathematics (STEM) teachers' intention to use mobile-based assessments in the teaching practice. The study proposes the teachers' acceptance mobile-based assessment (TAMBA) model which extends the technology acceptance model by introducing individual, social, institutional and instructional design factors. An appropriate questionnaire was developed and answered by 161 STEM teachers from 32 European countries. Their responses were analyzed using structural equation modeling. The proposed TAMBA model explains about 50% of the variance in teachers' intention to adopt mobile-based assessment. Perceived Ease of Use was found to be the most important determinant in teachers' intention to use mobile-based assessment. Facilitating Conditions and Output Quality were the most influential external variables in the model. The study findings revealed that focusing on mobile assessment quality design as well as on institutional support are important factors for STEM teachers in order to accept mobile-based assessments in schools.
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