Investigation of university students' perceptions of their eductors as role models and designers of digitalized curricula

ISSN: 1795-6889 www.humantechnology.jyu.fi Volume 16(1), February 2020, 55–91 55 INVESTIGATION OF UNIVERSITY STUDENTS’ PERCEPTIONS OF THEIR EDUCATORS AS ROLE MODELS AND DESIGNERS OF DIGITALIZED CURRICULA Abstract: Higher education graduates need 21st-century skills, both learning skills and competences for working with technology. However, research indicates an insufficient integration of ICTs into teaching and learning. In this paper, we examine students’ perception of various technology-based issues: (a) ICT integration within a Slovenian university’s learning environment, (b) teachers as role models for ICT use, and (c) the processes of collaboration and creativity as integrative parts featured in learning technologies. We studied beliefs about the contribution of ICT use to teaching and learning as the primary factors influencing ICT integration. A one-way ANOVA revealed that students in teacher education and education studies, as compared to students in other disciplines, perceive their teachers as effective designers of and as role models for ICT integration, although they do not perceive their teachers as leaders in new technology use. Effective leadership in technology innovation and the diversity of instructional design in guided and student-driven learning environments require continual curriculum development. Keywords: higher education, teacher education, information communication technology, educational technology, teacher as a role model, teacher educator. ©2020 Andreja Istenič Starčič & Maja Lebeničnik, and the Open Science Centre, University of Jyväskylä DOI: https://doi.org/10.17011/ht/urn.202002242163 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Andreja Istenič Starčič Faculty of Education University of Primorska Koper, Slovenia and Faculty of Civil and Geodetic Engineering University of Ljubljana Ljubljana, Slovenia and Institute of Psychology and Education Federal University of Kazan Kazan, Russia Maja Lebeničnik Faculty of Education University of Primorska Koper, Slovenia Istenič Starčič & Lebeničnik 56 INTRODUCTION Learning and innovation skills, the 4Cs (creativity, critical thinking, communication, and collaboration), skills for working with technology and in media-driven environments are, along with skills for work and life, integrative parts supporting subject-specific competences in the curriculum for the 21st century (Partnership for 21st-Century Skills [P21], 2012; P21 & American Association of Colleges of Teacher Education, [AACTE], 2010). Technology integration into teaching and learning makes an important contribution to graduates’ readiness for the workplace. It differs across sectors and professional disciplines, with the educational sector ranking only 14th of 22 on the Sector Digitalisation Index (Manyika, 2015). Worldwide, 21st-century skills have been applied to national curricula (see, e.g., Siddiq, Gochyyev, & Wilson, 2017). Technology-supported teaching and learning offer the potential for developing skills in critical thinking, problem-solving, and communication (Instefjord & Munthe, 2017), collaboration (Darling-Hammond, 2017; Jääskelä, Häkkinen, & Rasku-Puttonen, 2017), and creativity (Idris & Nor, 2010; Loveless, Burton, & Turvey, 2003; Sang, Valcke, van Braak, & Tondeur, 2010). Collaboration has been regarded as an integral part of technology-supported student-centered instruction and has widely been discussed (Means & Olson, 1997). The 2017 edition of the NMC [New Media Consortium] Horizon Report (Adams Becker et al., 2017) highlighted online collaboration as a means for developing 21st-century skills and technology adoption. In a series of recent reports on the essential learning technologies, noted educational consultant Donald Taylor (2017) twice (in 2015 and 2017) ranked collaborative and social learning at the top of his lists. Research in teacher education also has shown collaboration as an essential strategy in technology adoption (Hao & Lee, 2017; Hattie, 2009; Tondeur, van Braak, Siddiq, & Scherer, 2016). The NMC Horizon Report in 2017 revealed inequality in access existed even though online learning resources are available so widely (Adams Becker et al., 2017). In particular, teaching and learning approaches do not apply information communication technologies (ICTs) optimally (Jääskelä et al., 2017). Students increasingly use ICTs in most aspects of their lives and expect universities to address their needs and preferences for ICT use in the institutional learning environment (McGraw-Hill, 2017). Appropriate ICT use significantly affects students’ perceptions of the effectiveness of their courses (Venkatesh, Croteau, & Rabah, 2014). Prosser and Trigwell (2000) contended that students’ perception of the higher education learning environment should guide teachers’ pedagogical decisions because students’ perceptions are critical in their academic success. However, Croteau, Venkatesh, Beaudry, and Rabah (2015) identified a gap between teachers’ and students’ perceptions about the contribution of ICTs to learning, and Hammond (2014) determined a gap between rhetoric and reality in terms of studentcentered pedagogy in technology integration. A variety of reasons are reported for this:  teachers from diverse disciplines in higher education feel they lack the pedagogical competences for technology integration (Conole, Dyke, Oliver, & Seale, 2004);  the teachers’ training in ICTs for the classroom focused more on technical knowledge rather than an integrated approach, thus making it inadequate (Mishra, Koehler, & Kereluik, 2009);  a lack in dissemination of good practices (Ebert-May et al., 2011);  a lack of research and training for ICT integration at the tertiary level of education (Instefjord & Munthe, 2017); Students’ Perceptions of Educators as Role Models 57  a lack of understanding of higher education teachers’ beliefs about technologyintegrated teaching and learning (Jääskelä et al., 2017); and  higher education teachers’ negative beliefs about ICT integration for learning and insufficient interventions to transform them (Venkatesh et al., 2014). Higher education is tasked with preparing graduates for work life and participation in societies undergoing a rapid and sustained diffusion of new technologies, as well as addressing the current digital divide that, in the developed world, refers mainly to skills access and usage access (Van Dijk, 2006). As a result, higher education environments require the capability for fusing the academic and professional spaces with the students’ personal technology practices. The rate of integration of new technology depends on social and technical aspects and the users’ learning curve. Communication channels and social networks disperse technology innovation and facilitate imitation behaviors that contribute to ICT adoption (Cantono & Silverberg, 2009), with teachers serving as role models and curriculum developers toward this end (Bouckaert & Koos, 2017). Thus, higher education is being required to adopt ICTs within the academic environment and to apply professional and learning technologies within the curricula that offer an authentic learning experience. Such practices also enable students to become early adopters of new technologies in their professional fields. Integrating professional ICTs in curricula accelerates the learning curve of graduates in meeting the ICT requirements of the professional workplace. Differences in ICT implementation in teaching and learning exist within the various higher education professional disciplines (Croteau et al., 2015) and in students’ ICT skills (Owens & Lilly, 2017). It is important to examine differences in ICT-integrated teaching and learning among students of various academic disciplines and to discuss, in particular, the situation for student teachers and students in education studies who, throughout their professional careers, will influence the skills development of younger generations. Since the spread of microcomputers in the 1980s, courses on instructional design and technology increasingly focus on computer-based instruction, influenced by cognitivism and constructivism that facilitated student-centered instruction (Reiser, 2001). Newly qualified teachers need to meet the realities of current classrooms (Kessels & Korthagen, 2001), populated with students living in a digitalized world (Gudmundsdottir & Hatlevik 2018), and to engage teaching pedagogies informed by studentlearning approaches and digital practices (Istenič Starčič, Terlevic, Lin, & Lebeničnik 2018). Student teachers increasingly have access to digital resources, but significant diversity in teachers’ competences for technology integration is apparent (Gudmundsdottira & Hatlevikb, 2018). Often, a mismatch can be identified between what is expected of newly qualified teachers and their preservice preparation (Instefjord & Munthe, 2017). In this research, we examined whether a difference is apparent in the perceptions of the learning context and the teacher-asmodel among students in various higher education academic fields, ranging from education and the arts and humanities to business and law to engineering, math, and ICTs. Purpose of the Study The P21 discussions about 21st-century learning and innovation skills, collaboration, creativity, and skills for work with technology (Adams Becker et al., 2017) motivated our research design. This current study examined the academic learning environment in its function in preparing graduates for a working life infused with technology-driven environments. For successful digitalization of academic curricula, students’ perceptions and preferences should guide the Istenič Starčič & Lebeničnik 58 teacher’s pedagogical decisions (Prosser & Trigwell, 2000). Prior research among university students and teachers identified a large gap between students’ and teachers’ perceptions of the students’ use of technology in learning (Dahlstrom, 2015). Earlier research indicates that teachers are insufficient in serving as technology-use role models (Instefjord & Munthe, 2017) but indicates that teachers must assume responsibility as curriculum developers, not just curriculum transmitters (Bouckaert & Koos, 2017). Based on the reviewed literature, we designed our research objectives to examine students’ perception of the microlearning environment. The SQD model (synthesis of qualitative evidence; Tondeur et al., 2012, 2016) of the strategies for technology integration in teacher education informed our study. The SQD model defined factors in three levels: micro-, institutional, and system. In this study, we focused on the microlevel, which, according to Tondeur et al. (2016), consists of instructional design, authentic learning experience, reflection, feedback, collaboration, and the teacher as a role model. We drew from the SQD model in the framework of social cognitive theory, which defines learning as self-reflective and selfregulative in the process of interaction among the person, the behavior in learning situation context, and the environment (Bandura, 1986). The SQD model (Tondeur et al., 2012, 2016) refers to teachers arranging the learning environment and functioning as role models. In establishing the learning environment, the teacher is responsible for the design of curriculum materials, creating an authentic learning experience, facilitating collaboration among students, and demonstrating technology use. In our study, we examined the students’ perceptions of the learning environment by forming five research constructs: teacher’s ICT integration into teaching, the requirement of ICT use within the university’s learning environment, students’ ICT use for collaboration, students’ beliefs about ICTs, and the teacher as a role model. The social cognitive theory informed our study by its discussion of learning complex skills, vicariously by observing the role model’s components of action and behavior and “enactively” through actual performance (Bandura, 1971, 1986). According to social cognitive theory, the learning environment is influenced by a complex interplay between personal and sociocultural factors. How students construct their learning environment by selecting learning resources and how they are imposed by formal university curricula are integral parts of this complex interplay between the personal and social contexts. We examined the imposed, university-defined learning environment, known also as a guided environment, and the selected learning environment, which is chosen by students making decisions when creating their personal digital learning environments, a context also known as an unstructured environment (see also Lebeničnik & Istenič Starčič, 2018a). New technology provides great potential for fusion among academic, professional, and personal spheres, thereby constructing a unique learning environment that combines the selected and imposed environments. In this study, we examined students’ perceptions of the imposed and selected environments within the formed research construct of technology integration, the requirements within the university’s learning environment, ICTs for collaboration, and the role of the teacher as a model for ICT use (see Figure 1). Figure 1 represents our research model in concentric circles. In our study, we addressed 4C learning, innovation, and skills for work with technology, together which provide a basis for the 21st-century skills. The outside circle therefore highlights the 21st-century learning and innovation skills—which are creativity, critical thinking, communication, and collaboration— Students’ Perceptions of Educators as Role Models 59 Figure 1. Students’ perception of the microlearning environment with reference to enactive and vicarious learning for 21st-Century skills development. and skills for technology-supported learning. Inside the skills circle is a space with the components affecting students’ ICT use, which we designed as research constructs for our study: Students’ perception of the Teacher as a Role Model, the University’s Requirements for ICT Use, and the Teacher’s ICT Integration in Teaching, Students’ Beliefs about ICTs, and Students’ ICT Use for Collaboration. These are also names of the measurement scales that we designed for our study. In the inner circle, the teacher, student, and digitalized curriculum provide the basis for vicarious and enactive learning. Above the concentric circles, the selected learning environment embodies the student’s unstructured environment, whereas beneath the circles is the imposed learning environment focusing more on learning environment guided and provided by the university. Technology Integration in the Higher Education Learning Environment Technology integration into teaching and learning in university learning environments makes an important contribution to students’ development of competences and skills (Adams Becker et al., Istenič Starčič & Lebeničnik 60 2017; Jääskelä et al., 2017). This requires digital competences to be integrated across curricula at all educational levels. Three possible approaches are applicable for developing competences and skills for ICT use in the professional setting and in the civic and personal spheres of life: 1. The curricular approach involves offering specific courses in computing and information technology. 2. The cross-curricular approach integrates computing and information technology into subject-specific professional-development areas. This integrated approach provides a greater degree of application and sustainability than does the curricular. 3. The most widespread extracurricular practices are informal, self-directed learning approaches that employ online learning resources and collaboration; such informal approaches should be integrated into the formal curriculum (Adams Becker et al., 2017). Integrating learning technology into the higher education environment, a process that requires a sharp learning curve, has slower adoption and higher rejection by faculty (Underwood & Dillon, 2011). Chandra and Mills (2015) discussed adoption strategies and noted the significant potential for merging ICTs seamlessly into education as well as niche uses of ICTs. Technologies could merge seamlessly with or transform pedagogical practices in learning environment design, within the collaboration between the teacher and students, and among students themselves (Adams Becker et al., 2017). Digitalizing the higher education curriculum involves reflective practices in planning and designing online learning content and lesson performance (Conole et al., 2004), utilizing artificial intelligence and a variety of sources of data for tracking students’ learning processes comprehensively, as well as aligning teaching strategies and feedback that affect students’ learning approaches (Gašević & Siemens, 2015). Integrating contemporary technologies and artificial intelligence into education supports insights into the learning process and facilitates reflection on teachers’ roles (Istenič Starčič, 2019). This perspective can challenge current beliefs on ICTs in teaching and learning, yet captures the authenticity of ICTs in students’ life experiences and professional practices. Authenticity in teaching is achieved by integrating students’ authentic existent ICT practices with anticipated professional ICT practices. Students’ perceptions of the higher education learning environment is critical for their selection of learning approaches and consequent academic success; these perceptions also provide essential information for teachers’ planning and instructional designs (Prosser & Trigwell, 2000). Digitalized curricula require a diversity of online learning resources, activities, and student skills. However, a divide often is apparent between imposed and selected learning environments. Universities use learning management systems, which tend to be university driven, while students use social media, which are more student driven (Dabbagh & Kitsantas, 2012). Apart from providing reading packages, students are made aware of the great variety of online resources available to them to enhance their learning; students’ can select these autonomously from the course curriculum (Lebeničnik & Istenič Starčič, 2018a, 2018b). Therefore, a university’s readiness for and ability to integrate a variety of digital channels to connect the imposed and selected learning environments will support the students’ development of 21st-century skills. Online learning resources are beneficial for the flipped classroom model by supplementing the two integral modes of flipped learning: (a) prior classroom preparation and (b) interactive classroom work. Prior to a classroom lecture, online resources are studied at home; then, during classroom work, active learning methods are applied utilizing the online learning Students’ Perceptions of Educators as Role Models 61 resources. Sun, Xie, and Anderman (2018), in their overview of the flipped classroom, highlighted the contribution of Internet-based learning (i.e., online instructional videos and text readings) versus traditional face-to-face instruction. These authors argued for self-directed preclass learning to increase the quality of students’ performance in a flipped classroom and for their overall learning outcomes (Sun et al., 2018). They indicated a need for a transition from the traditional transmissive lectures to supporting self-directed preparation prior to classroom work, which also serves as a basis of knowledge and a form of interactive learning. For both preclass preparation and interactive classroom work, online learning resources could be used as a combination of the selected and imposed learning environments. A virtual extension of the physical classroom also requires university teachers to use technology to support students in developing community among themselves and providing collaborative activities online outside the classroom (Dabbagh & Kitsantas, 2012). Appropriate instructional or institutional support within a formal curriculum where students bring their own devices (BYOD) also is an important facilitator in the formation of personal learning environments. Such processes connect learning during lectures with learning outside lectures and tutorials (Adams Becker et al., 2017), as well as reduce the intention– behavior gap in learning (Crossler, Long, Loraas, & Trinkle, 2014). When considering the components of initial teacher education, ICT integration is critical in teaching and learning (Drent & Meelissan, 2008). Therefore, teacher education should provide authentic experiences (Reeves, Herrington, & Oliver, 2002; Tondeur et al., 2016) that expose students to a variety of teaching strategies and classroom activities (Hattie, 2009), a diversity of outcomes, and seamless integration into assessment (Reeves et al., 2002). Authentic learning experiences also require an integrated approach connecting technological, pedagogical, and content knowledge (Mishra et al., 2009) and connect the learner to the social context where the technology is applied (Istenič Starčič & Turk, 2016). As a result, in line with Tondeur’s SQD model (Tondeur et al., 2012, 2016), influencing factors at the microlevel are integrated into the contexts at the institutional and system levels. Teacher-Educator as a Role Model Students’ observation of their teacher’s behavior during the learning process is an essential component of their education (Kelcherman, 2009). Concerning the inclusion of ICTs in classroom teaching, how much a teacher’s behavior influences students’ use of technologies for learning is uncertain. Based on a survey among preservice teachers in Flanders, Belgium, Tondeur et al. (2016) stressed that the teacher-educator as a role model in technology use is critical, as their research suggested teacher-educators seem not to provide sufficient modeling. Lai (2015) mapped teachers’ behavior in technology integration, assessing also the teacher’s influence on students’ technology use outside the classroom. He highlighted a combination of roles the teacher plays on three levels: affection (i.e., encouragement and enhancing awareness for technology use), capacity (i.e., use recommendations and tips), and behavioral support (i.e., the teacher serves as a model for technology use). Thus the teachers’ role in technology integration needs close attention. Teachereducators perform many roles, and in technology integration, we highlight two: as a role model in ICT use with actual use behavior and as curriculum developer offering experiences concerning a digitalized curriculum (Bouckaert & Koos, 2017). Katyal and Evers (2004) confirmed the two levels of teachers’ activity explicitly through instructional practice and implicitly by role modeling Istenič Starčič & Lebeničnik 62 (see also Lai, 2015). Because technology integration requires addressing technological, pedagogical, and content knowledge (Mishra et al., 2009), role modeling requires actual use of technology in teaching and curriculum design (Tondeur et al., 2016). Student learning from observing role models or from the implicit modeling provided through the design of the curriculum is essential in preparing for technology integration (Milrad, Spector, & Davidsen, 2002) in professional contexts. Modeling also provides one of the most important sources for transmitting values and attitudes (Bandura, 1986). Teachers should introduce new technological solutions by modeling their professional use of ICTs, thus acting as role models and facilitating observational learning. Vicarious learning occurs through students observing actual performance or by symbolic models in the absence of overt performance. Such processes enhance learning better than if students have to perform every action one at a time to learn it. The rapidly changing social context concerning technology developments and younger generations increasingly adopting new technologies into their day-to-day lives require leadership by teachers in the field. Teacher-educators increasingly are responsible for integrating technological innovations into the initial preservice curriculum development and for modeling technology integration through continual curriculum development (Bouckaert & Koos, 2017; Tondeur et al., 2016), providing authentic experiences through applying a set of strategies (Tondeur et al., 2016) that support changes at the levels of attitudes and behavior (Instefjord & Munthe, 2017). Based on Bandura’s (1986) research, teacher-educators transmit values and attitudes through modeling. Thus, teachers’ actions could directly inhibit or facilitate students’ actions, while the teachers advance learning through modeling, that is, students develop new behaviors based on observation of their teachers’ modeling (Schunk, 2012, pp. 127). By observing their teachers’ interactions with technology and participating in digitalized curriculum learning activities designed by their teachers, student teachers acquire experiences in technology use that influence the development of attitudes, beliefs, and motivation for using ICTs during their initial education, which in turn encourages their future pedagogical technology integration. Collaborative and Creative Aspects of Learning and Students’ Beliefs About ICT Use Technology-supported learning environments facilitate critical thinking, collaboration skills (Jääskelä et al., 2017), and creativity (Idris & Nor, 2010; Loveless et al., 2003; Sang et al., 2010). Such learning environments also could reduce the divide between academic and professional behaviors in real-life contexts (Istenič Starčič et al., 2018). Collaboration is among the main affordances of ICTs (Conole & Dyke, 2004) and is essential in student-centered technology-supported learning (Means & Olson, 1997). Information sharing and networking form the basis for the information society and the networked society. Computer-supported collaborative learning, which rose in 1980s, continues to be a main trend in higher education (Adams Becker et al., 2017). Thus, the higher education classroom extends outward from the physical classroom through the spread of online resources, most of which are predominantly collaborative in nature. Karakaya and Demirkan (2015) and Muldner and Burleson (2015) examined these collaborative digital environments as means for enhancing creativity. Creativity in learning is augmented when students are curious and excited (Torrence & Goff, 1990), and technologysupported learning increases the level of students’ creativity in learning (Sang et al., 2010). Idris Students’ Perceptions of Educators as Role Models 63 and Nor (2010) examined ICT integration into learning with reference to excitement, motivation, and curiosity, which enhance learning. Flexible environments, facilitated by the affordances of ICTs, also support creativity (Davies et al., 2013). Darling-Hammond (2017) discussed teacher preparation in a 21st-century curriculum based on collaboration and interactive computer technology for meaningful learning. In teacher education and education studies, group work and collaboration with peers are essential when preparing them for technology integration in teaching (Hao & Lee, 2017: Hattie, 2009; Tondeur et al., 2016), as well as providing opportunities for engagement and reflection (Tondeur et al., 2016). Teaching and learning should engage students in authentic technology practices such as social network practices, which parallel students’ authentic social practices (Istenič Starčič et al., 2018). Collaboration and online sharing behaviors also support vicarious learning (Bandura, 1986; Schunk, 2012), which could contribute to technology adoption (Adams Becker et al., 2017) into other spheres of their lives and enhance their beliefs about technology integrated into learning and teaching. The use of social collaboration practices among peers and other groups facilitates the formation of beliefs, which in turn, guides behavior (Ajzen, 2001). Regarding meaningful learning, we also examined the relationship between beliefs and technology use, focusing on collaborative learning as an integral part of student-centered learning (Ertmer, Ottenbreit-Leftwich, Sadik, Sendurur, & Sendurur, 2012; Means & Olson, 1997). Ertmer et al. (2012) concluded that beliefs regarding student-centered learning correspond with collaborative technology-supported learning. Beliefs are regarded as the most influential internal factors of behavior (Ertmer et al., 2012), influencing, in the case of educational behavior, the probability of pedagogical change (Hao & Lee, 2017). Sang et al. (2010) established that attitudes toward technology in education among student teachers are the strongest predictor of future use. Preservice education could impact significantly a student’s internal factors (Paratore, O’Brien, Jiménez, Salinas, & Ly, 2016) and is therefore critical for developing future teachers’ beliefs about technology integration in teaching and learning (Drent & Meelissan, 2008). Beliefs are the mediating factor that build the framework of values embedded within the learning process and skills development. Whether or not preservice teachers integrate the value of ICTs into their beliefs will influence their prospective technology integration (Chen, 2010). Research has demonstrated that, among university students, beliefs toward ICT use in learning is one of the main predictors for technology use (Lai, Wang, & Lei, 2012). Thus, beliefs are strong influencing factors in relation to one’s attitudes toward (Ajzen, 2001), perceived capabilities of (Bandura, 1986), and perceived characteristics of ICT in learning. Beliefs predict behavior as predisposition to respond favorably or unfavorably toward an object or objective (Ajzen, 1988). In related studies, Sang et al. (2010) identified several key research-instrument scale items for beliefs toward ICT use in education: improving learning performance, efficiency, individualization and differentiation in learning, and the level of creativity in students. Van Braak and Tearle (2007) identified three more: (a) observability, referring to ICT use allowing teachers and peers to see outcomes; (b) specificity, referring to ICTs supporting tasks that otherwise are not possible; and (c) flexibility with ICTs, providing learning activities performed with greater adaptability. In related studies, outcomes of beliefs regarding ICTs in education include ICT usage, motives toward accessing information, increased interaction, and networking and collaboration (LaRose & Eastin, 2004; Senkbeil & Ihme, 2017). Istenič Starčič & Lebeničnik 70 Table 5. ANOVA and Post Hoc Test Results for Teacher’s ICT Integration in Teaching Corresponding to the Frequency of ICT Use by Teachers. Note. UT–University teaching; TEES–Teacher Education and Education Studies; AH–Arts and Humanities; SSBAL–Social sciences, Business, administration and law; NSMI–Natural sciences, mathematics, and ICTs; EMC–Engineering, manufacturing and construction; HW–Health and welfare. p ˂ .05 is in bold; n.s.–nonsignificant, Items Study field– KLASIUS-P M SD Between-group difference (Post hoc test /pairwise comparison) UT1. Enhancing bring your own device (BYOD) during classroom activities. F(5) = 9.699, p = 0.084 TEES 2.52 1.08 n.s. AH 3.06 1.27 SSBAL 2.79 0.95 NSMI 2.86 1.11 EMC 2.80 1.10 HW 2.53 0.86 Total 2.76 1.07 UT2. ICT use during classroom lectures and tutorials for students’ activities (e.g., computer-supported collaborative learning). χ² = 19.150, p = 0.002 TEES 3.25 0.98 HW < TEES (p = 0.013, d = 0.47), HW < SSBAL (p = 0.002, d = 0.44) AH 3.00 1.00 SSBAL 3.24 1.08 NSMI 3.03 1.15 EMC 2.99 1.06 HW 2.77 1.02 Total 3.04 1.07 UT3. ICT use outside classroom lectures and tutorials for students’' activities (e.g., computer-supported collaborative learning). F(5) = 7.890, p = 0.162 TEES 2.81 1.13 n.s AH 2.96 1.04 SSBAL 3.05 1.12 NSMI 3.07 1.17 EMC 3.08 1.05 HW 2.72 1.06 Total 2.97 1.10 UT4. ICT use outside classroom lectures for learning content prior to organized class lectures. χ² = 17.938, p = 0.003 TEES 2.84 1.11 HW < SSBAL (p = 0.006, d = 0.47), HW < ECM (p = 0.047, d = 0.46) AH 3.11 1.00 SSBAL 3.27 1.14 NSMI 3.10 1.12 EMC 3.25 1.11 HW 2.76 1.00 Total 3.09 1.09 UT5. ICT use for feedback and assessment. χ² = 13.051, p = 0.023 TEES 2.56 1.03 n.s AH 2.69 1.17 n.s SSBAL 2.93 1.21 NSMI 3.07 1.20 EMC 3.01 1.09 HW 2.68 0.89 Total 2.89 1.13 Students’ Perceptions of Educators as Role Models 71 Post hoc testing indicated HW students (M = 2.77, SD = 1.02) use ICTs less frequently during classroom lectures and as tutorials for student activities (e.g., computer-supported collaborative learning) than do TEES (M = 3.25, SD = 1.98) and SSBAL (M = 3.24, SD = 1.08) students. ICT use outside classroom lectures for learning content prior to organized class lectures was significantly less frequent for HW students (M = 2.76, SD = 1.00) than for SSBAL (M = 3.27, SD = 1.14) and EMC (M = 3.25, SD = 1.11) students. There was a significant difference in ICT use for feedback and assessment; however, post hoc testing did not identify significant differences between groups. No significant differences were found in BYOD and ICT use outside classroom lectures and tutorials for student activities. A Difference Among Disciplines in Students’ Perception of University’s Requirements for ICT Use Students assessed the frequency of items in teaching and learning on an interval scale (1–Never, to 5–Very frequently). A statistically significant difference between the disciplines is apparent in all four scale items, thereby confirming H2 (Table 6). Post hoc tests identifying pairwise difference with small effect size in almost all cases. The pairwise difference with medium effect size was identified in the “I am required to use ICTs for data analysis” between AH and SSBAL (d = 0.64) and between AH and NSMI (d = 0.69). TEES students had higher means (M = 3.18, SD = 1.25) for the item “I am required to use ICT for independent study,” whereas we found a significant difference between groups, χ²(5) = 16.302, p = 0.006. Post hoc testing indicated that AH students (M = 2.77, SD = 1.23) use ICTs significantly less often for independent study than do students of TEES (M = 3.18, SD = 1.25) and NSMI (M = 3.14, SD = 1.20). TEES students also had higher means (M = 2.17, SD = 1.05) for “I am required to use ICT for which I do not get adequate training,” and there was a significant difference between groups, χ²(5) = 21.142, p = 0.001. Post hoc testing indicated AH students (M = 1.88, SD = 1.11) are required to use technology for which they do not get adequate training significantly less frequently than are students of TEES (M = 2.17, SD = 1.05) and EMC (M = 2.21, SD = 1.13). TEES students (M = 2.78, SD = 1.19) also had the second highest mean in “I am required to use ICT for computer supported collaborative learning;” a significant difference between groups also was found, χ²(5) = 57.192, p = 0.000. Post hoc testing indicated AH students (M = 2.15, SD = 1.10) use technology for collaborative learning less frequently than do students of TEES (M = 2.78, SD = 1.19), EMC (M = 2.39, SD = 1.15), HW (M = 2.60, SD = 1.19), NSMI (M = 2.66, SD = 1.23), and SSBAL (M = 2.93, SD = 1.25). Post hoc testing also indicated that EMC students (M = 2.39, SD = 1.15) use technology for collaborative learning less frequently than do students of TEES (M = 2.78, SD = 1.19) and SSBAL (M = 2.93, SD = 1.25). Post hoc testing indicated a significant difference for “I am required to use ICT for data analysis,” χ² (5) = 71.728 p = 0.000. Post hoc testing indicated AH students (M = 2.49, SD = 1.19) use data analysis less frequently than do students of TEES (M = 3.03, SD = 1.11), EMC (M = 2.89, SD = 1.19), HW (M = 2.95, SD = 1.14), SSBAL (M = 3.26, SD = 1.21), NSMI (M = 3.32, SD = 1.21). Post hoc testing indicated EMC students (M = 2.89, SD = 1.19) are using data analysis less frequently than are students of SSBAL (M = 3.26, SD = 1.21) and NSMI (M = 3.32, SD = 1.21). Post hoc testing also revealed that HW students (M = 2.95, SD = 1.14) use data analysis less frequently than do NSMI (M = 3.32, SD = 1.21) students. Istenič Starčič & Lebeničnik 72 Table 6. ANOVA and Post Hoc Test Results for in Students’ Perception of University’s Requirements for ICT Use. Items Study field - KLASIUS-P M SD Between-group difference (Post hoc test /pairwise comparison) UR1. I am required to use ICTs for which I do not get adequate training. χ² = 21.142, p = 0.001 TEES 2.17 1.05 AH < TEES (p = 0.018, d = 0.26), AH < ECM (p = 0.005, d = 0.29) AH 1.88 1.11 SSBAL 1.93 1.02 NSMI 2.04 1.07 EMC 2.21 1.13 HW 1.88 0.96 Total 2.01 1.06 UR2. I am required to use ICTs for computer-supported collaborative learning. χ² = 57.192, p = 0.000 TEES 2.78 1.19 AH < TEES (p = 0.001, d = 0.02) AH < HW (p = 0.005, d = 0.21) AH < NSMI (p = 0.001, d = 0.23) AH < SSBAL (p = 0.001, d = 0.36) ECM < TEES (p = 0.044, d = 0.33) ECM < SSBAL (p = 0.001, d = 0.45) AH 2.15 1.10 SSBAL 2.93 1.25 NSMI 2.66 1.23 EMC 2.39 1.15 HW 2.60 1.19 Total 2.59 1.21 UR3. I am required to use ICTs for data analysis. χ² = 71.728, p = 0.000 TEES 3.03 1.11 AH < TEES (p = 0.001, d = 0.46) AH < EMC (p = 0.008, d = 0.33) AH < HW (p = 0.006, d = 0.39) AH < SSBAL (p = 0.001, d = 0.64) AH < NSMI (p = 0.001, d = 0.69) EMC < SSBAL (p = 0.019, d = 0.31) EMC < NSMI (p = 0.002, d = 0.35) HW < NSMI (p = 0.018, d = 0.31) AH 2.49 1.19 SSBAL 3.26 1.21 NSMI 3.32 1.21 EMC 2.89 1.19 HW 2.95 1.14 Total 3.04 1.22 UR4. I am required to use ICTs for independent study. χ² = 16.302, p = 0.006 TEES 3.18 1.25 AH < TEES (p = 0.041, d = 0.33) AH < NSMI (p = 0.013, d = 0.30) AH 2.77 1.23 SSBAL 3.06 1.22 NSMI 3.14 1.20 EMC 2.90 1.20 HW 3.06 1.23 Total 3.01 1.23 Note. UR–University Requirements; TEES–Teacher Education and Education Studies; AH–Arts and Humanities; SSBAL–Social sciences, Business, administration and law; NSMI–Natural sciences, mathematics, and ICTs; EMC–Engineering, manufacturing and construction; HW–Health and welfare. p ˂ .05 is in bold; n.s.–nonsignificant. A Difference Among Disciplines in Students’ Perception of Teacher as a Role Model Students marked their agreement with statements on a five-point Likert scale (1–Totally disagree to 5–Totally agree). We found a statistically significant difference between the various disciplines in all scale items and H3 therefore is confirmed (Table 7). Post hoc tests identifying pairwise difference with small effect size in all items. On average, students in total gave negative to neutral values for teachers as role models in ICT use (2.55 ≤ M ≥ 3.30). The highest value was for “Faculty is successful in using ICT for Students’ Perceptions of Educators as Role Models 73 Table 7. ANOVA and Post Hoc Test Results for Students’ Perception of Teacher as a Role-Model. Items Study field KLASIUS-P M SD Betweengroup difference (Post hoc test /pairwise comparison) TM1. Faculty is successful in using ICTs for teaching. F(5) = 3.165, p = 0.008 TEES 3.37 0.98 NSMI > HW (p = 0.011, d = 0.30) AH 3.20 0.99 SSBAL 3.35 1.00 NSMI 3.43 0.96 EMC 3.24 1.02 HW 3.12 1.06 Total 3.30 1.00 TM2. Faculty uses innovative technological solutions. F(5) = 4.373, p = 0.001 TEES 2.80 1.02 NSMI > TEES (p = 0.028, d = 0.29) NSMI > AH (p = 0.007, d = 0.31) NSMI > HW (p = 0.044, d = 0.26) AH 2.78 0.97 SSBAL 3.02 1.08 NSMI 3.10 1.03 EMC 3.04 1.00 HW 2.82 1.05 Total 2.96 1.03 TM3. I look to faculty for how to resolve problems that may occur during ICT use. χ² = 24.109, p = 0.000 TEES 2.44 0.96 HW < SSBAL (p = 0.050, d = 0.27) HW < EMC (p = 0.007, d = 0.36) HW < NSMI (p = 0.001, d = 0.41) AH 2.44 0.94 SSBAL 2.56 1.11 NSMI 2.70 1.11 EMC 2.63 1.00 HW 2.27 0.96 Total 2.55 1.04 TM4. Faculty show enthusiasm for using technological solutions. F(5) = 5.707, p = 0.000 TEES 2.63 1.04 NSMI > TEES (p = 0.002, d = 0.36) NSMI > AH (p = 0.028, d = 0.27) NSMI > SSBAL (p = 0.031, d = 0.25) TEES < ECM (p = 0.033, d = 0.32) AH 2.73 1.02 SSBAL 2.75 1.12 NSMI 3.02 1.12 EMC 2.97 1.08 HW 2.63 1.08 Total 2.82 1.09 TM5. Faculty is at least as competent as I am in using ICTs. F(5) = 7.299, p = 0.000 TEES 3.05 1.09 NSMI > AH (p = 0. 001, d = 0.41) NSMI > SSBAL (p = 0.002, d = 0.29) NSMI > HW (p = 0. 001, d = 0.44) HW < EMC (p = 0.047, d = 0.30) AH 2.82 1.03 SSBAL 2.93 1.12 NSMI 3.26 1.11 EMC 3.09 1.04 HW 2.77 1.08 Total 3.01 1.10 Note. TM-Teacher-Model; TEES–Teacher Education and Education Studies; AH–Arts and Humanities; SSBAL–Social sciences, Business, administration and law; NSMI–Natural sciences, mathematics, and ICTs; EMC–Engineering, manufacturing and construction; HW–Health and welfare. p ˂ .05 is in bold; n.s.–nonsignificant; Istenič Starčič & Lebeničnik 74 teaching” (M = 3.30, SD = 1.00), and the lowest was “I look to faculty for how to resolve problems that may occur during ICT use” (M = 2.55, SD = 1.04). TEES students had lower means in most items relative to students in other disciplines. In one item, however, TEES students’ mean was among the highest—“Faculty is successful in using ICT for teaching”—although in this item, TEES students were not significantly different from students of other disciplines. There was a statistically significant difference for “Faculty is successful in using ICT for teaching,” as determined by one-way ANOVA, F(5) = 3.165, p = 0.008. Post hoc testing revealed that NSMI students (M = 3.43, SD = 0.96) more strongly agreed that faculty is successful in using ICTs for teaching than did HW students (M = 3.12, SD = 1.06). A one-way ANOVA determined a statistically significant difference between groups for “Faculty use innovative technological solutions,” F(5) = 4.373, p = 0.001. Post hoc testing of students of different study programs revealed that students of NSMI (M = 3.10, SD = 1.03) agreed less strongly that faculty use innovative technological solutions than did students in TEES (M = 2.80, SD = 1.02), AH (M = 2.78, SD = 0.97), and HW (M = 2.82, SD = 1.05). However, HW students (M = 2.82, SD = 1.05) agreed less strongly with this statement than did EMC students (M = 3.04, SD = 1.00). Kruskal-Wallis tests identified a difference between groups for “I look to faculty for how to resolve problems that may occur during ICT use, χ²(5) = 24.109, p = 0.000. Post hoc testing indicated that HW students (M = 2.27, SD = 0.96) agreed less strongly with this statement than did SSBAL (M = 2.56, SD = 1.11), EMC (M = 2.63, SD = 1.00), and NSMI (M = 2.70, SD = 1.11) students. Difference between groups for “Faculty show enthusiasm for using technological solutions” was statistically significant, as determined by one-way ANOVA, F(5) = 5.707, p = 0.000). Post hoc testing indicated NSMI students (M = 3.02, SD = 1.12) agreed more strongly with this statement than did TEES (M = 2.63, SD = 1.04), AH (M = 2.73, SD = 1.02), SSBAL (M = 2.75, SD = 1.12), and HW (M = 2.63, SD = 1.08) students. There was also a statistically significant difference between students of TEES (M = 2.63, SD = 1.04) and EMC (M = 2.97, SD = 1.08). The same was true between groups for “Faculty is at least as competent as I am in using ICTs,” as determined by a one-way ANOVA, F(5) = 7.299, p = 0.000). Post hoc testing revealed that students of NSMI (M = 3.26, SD = 1.11) agreed more strongly with this statement than did students of AH (M = 2.82, SD = 1.03), SSBAL (M = 2.93, SD = 1.12), or HW (M = 2.77, SD = 1.08). There was also a statistically significant difference between HW (M = 2.77, SD = 1.08) and EMC (M = 3.09, SD = 1.04) students. A Difference Among Disciplines in Students’ ICT Use for Collaboration Students assessed the frequency of items on an interval scale (1–Never, to 5–Very frequently). A statistically significant difference between different disciplines was found in four scale items. In six, there was no statistically significant difference, and H4 therefore is partly confirmed (Table 8). Post hoc tests identifying pairwise difference with small effect size in item 5. In item one, there was no effect between NSMI and SSBAL (d = 0.19), in item 2, there was no effect between NSMI and SSBAL (d = 0.15) and in item 5, there was no effect between NSMI and TEES (d = 0.17). Students’ Perceptions of Educators as Role Models 75 Table 8. ANOVA and Post Hoc Test Results for Students’ ICT Use for Collaboration. Items Study field - KLASIUS-P M SD Between-group difference (Post hoc test /pairwise comparison) C1. I seek help online if I have a problem. χ² = 12.367, p = 0.030 TEES 2.55 1.25 NSMI < SSBAL (p = 0.027, d = 0.19) AH 2.73 1.34 SSBAL 2.80 1.24 NSMI 2.55 1.36 EMC 2.71 1.22 HW 2.61 1.30 Total 2.66 1.29 C2. I share information related to my courses on social media sites. χ² = 14.177, p = 0.015 TEES 2.11 1.15 NSMI < SSBAL (p = 0.041, d = 0.15) AH 2.21 1.19 SSBAL 2.23 1.20 NSMI 2.04 1.20 EMC 2.04 1.14 HW 2.19 1.20 Total 2.13 1.18 C3. I participate in social network sites discussions from my study field. F(5) = 5.160, p = 0.397 TEES 2.09 1.23 n.s AH 2.20 1.18 SSBAL 2.28 1.19 NSMI 2.23 1.28 EMC 2.22 1.30 HW Total 2.17 2.21 1.14 1.22 C4. I follow the educational content suggested by computer recommendation systems (e.g., on multimedia platforms, social networks, online news). χ² = 8.333, p = 0.139 TEES 2.11 1.18 n.s AH 2.28 1.12 SSBAL 2.38 1.12 NSMI 2.26 1.36 EMC 2.17 1.18 HW 2.21 1.08 Total 2.25 1.11 C5. I participate actively in online communities in my study field where I know the majority of participants. χ² = 18.089, p = 0.003 TEES 3.70 1.41 AH < TEES (p = 0.033, d = 0.24) SSBAL < TEES (p = 0. 036, d = 0.25) NSMI < TEES (p = 0.027, d = 0.17) AH 3.36 1.41 SSBAL 3.34 1.37 NSMI 3.46 1.36 EMC 3.46 1.30 HW 3.70 1.28 Total 3.49 1.36 C6. I use synchronous communication e-tools for communicating with other students while learning (e.g., Skype, Facebook Messages, gTalk, Viber). χ² = 10.077, p = 0.073 TEES 3.46 1.78 n.s AH 3.23 1.37 SSBAL 3.53 1.76 NSMI 3.40 1.94 EMC 3.34 1.82 HW 3.60 1.47 Total 3.44 1.34 Istenič Starčič & Lebeničnik 76 Table 8. ANOVA and Post Hoc Test Results for Students’ ICT Use for Collaboration (continued) C7. I post blogs on the Web with content from my study field (e.g., long posts on social networks, a stand-alone blog, use of blog platforms, online weblog writing). χ² = 7.144, p = 0.210 TEES 1.42 .72 n.s AH 1.64 1.11 SSBAL 1.57 0.95 NSMI 1.49 0.86 EMC 1.48 0.78 HW 1.48 0.74 Total 1.52 0.94 C8. I co-create documents for my course learning (e.g., Google Docs, Wiki). χ² = 19.195, p = 0.002 TEES 2.51 1.36 ECM < SSBAL (p = 0.001, d = 0.30) NSMI < SSBAL (p = 0.039, d = 0.21) AH 2.59 1.37 SSBAL 2.92 1.44 NSMI 2.61 1.39 EMC 2.49 1.34 HW 2.86 1.39 Total 2.64 1.39 C9. I use news aggregators (RSS feed, e.g., feedly.com). χ² = 10.738, p = 0.057 TEES 1.31 0.65 n.s AH 1.41 0.66 SSBAL 1.43 0.69 NSMI 1.42 0.83 EMC 1.42 0.63 HW 1.27 0.42 Total 1.39 0.82 C10. I share my own files for learning with others (e.g., Google Drive, Dropbox). F(5) 10.435, p = 0.064 TEES 3.39 1.30 n.s AH 3.11 1.34 SSBAL 3.31 1.31 NSMI 3.23 1.30 EMC 3.41 1.26 HW 3.23 1.23 Total 3.27 1.30 Note. C–Collaboration; TEES–Teacher Education and Education Studies; AH–Arts and Humanities; SSBAL–Social sciences, Business, administration and law; NSMI–Natural sciences, mathematics, and ICTs; EMC–Engineering, manufacturing and construction; HW–Health and welfare. p ˂ .05 is in bold; n.s.–nonsignificant; TEES students had lower means relative to students of other disciplines only in 4 items of 10. The highest mean for TEES students was for “I participate actively in an online community of my study field where I know the majority of participants.” TEES students also had a high mean for “I use computer-supported collaborative learning” and for “I share my own files for learning with others.” Kruskal-Wallis testing indicated significant differences for participating in online communities where participants are known, χ²(5) = 18.089, p = 0.003. Post hoc testing indicated AH (M = 3.36, SD = 1.41), SSBAL (M = 3.34, SD = 1.37), and NSMI (M = 3.46, SD = 1.36) students participated less frequently in online communities than did TEES students (M = 3.70, SD = 1.41). Further, Kruskal-Wallis testing indicated significant differences for co-creating documents for course learning, χ²(5) = 19.195, p = 0.002. Post hoc testing indicated EMC students (M = Students’ Perceptions of Educators as Role Models 77 2.49, SD = 1.34) co-created groups less frequently than did students of SSBAL (M = 2.92, SD = 1.44). Post hoc testing also indicated NSMI students (M = 2.61, SD = 1.39) co-created groups less frequently than did SSBAL students (M = 2.92, SD = 1.44). Kruskal-Wallis testing indicated significant differences for “I seek help online if I have a problem,” χ²(5) = 12.367, p = 0.030. In this area, NSMI students (M = 2.55, SD = 1.36) revealed they seek help less frequently than do SSBAL students (M = 1.24, SD = 2.80). Kruskal-Wallis testing indicated significant differences for “I share information related to my courses on social media sites” χ²(5) = 14.177, p = 0.015). NSMI students (M = 2.04, SD = 1.20) share information less frequently than do SSBAL students (M = 2.23, SD = 1.20). We found no difference in the following items: “Participating in SNS discussions in my study discipline” and “Sharing own files for learning.” A Difference Among Disciplines in Student’s Beliefs About ICT Use Students marked their agreement with statements on a Likert scale (1–Totally disagree to 5–Totally agree). We found a statistically significant difference between study disciplines in eight items (Table 9). In seven items, there were no differences, therefore confirming H5 partly. In 9 items of 15, TEES students had lower means than did students of other disciplines. Post hoc tests identifying pairwise difference with small effect size in majority of items. The pairwise difference with medium effect size was identified in the “Learning with ICTs is more fun than traditional learning.” between TEES and SSBAL (d = 0.66) and between AH and SSBAL (d = 0.55), EMC and SSBAL (d = 0.77). The pairwise difference with medium effect size was identified in the “Information gathered online is better than information from other sources” between AH and HW (d = 0.67) and between AH and SSBAL (d = 0.55). In two items there was no effect, in item 5, between AH and HW (d = 0.18), and in item 13, between NSMI and SSBAL (d = 0.17). Kruskal-Wallis testing indicated significant differences for the belief “Learning with ICT is more fun than traditional learning,” χ²(5) = 20.880, p = 0.001. Post hoc testing revealed TEES students (M = 3.10, SD = 1.10) believed this more strongly than did SSBAL students (M = 2.36, SD = 1.13) but significantly more weakly than did HW students (M = 3.41, SD = 1.29). Post hoc testing revealed that AH students (M = 3.03, SD = 1.30) and EMC students (M = 3.17, SD = 0.96) also believed this more strongly than did SSBAL students (M = 2.36, SD = 1.13). Additionally, AH (M = 3.03, SD = 1.30), EMC (M = 3.17, SD = 0.96), and NSMI (M = 3.22, SD = 1.21) students believed this more weakly than did HW students (M = 3.41, SD = 1.29). There was a statistically significant difference between groups as determined by one-way ANOVA for “I have better grades because of use of ICTs for learning,” F(5) = 21.137, p = 0.001. Post hoc testing identified that TEES students believed this less strongly (M = 2.84, SD = 1.12) than did HW students (M = 3.10, SD = 1.07), EMC students (M = 3.12, SD = 1.13), SSBAL students (M = 3.17, SD = 1.01), or NSI students (M = 3.22, SD = 1.21). Less strong beliefs were identified also for AH students (M = 2.91, SD = 1.24) as compared to SSBAL students (M = 3.17, SD = 1.01) and NSI students (M = 3.22, SD = 1.21). Kruskal-Wallis testing indicated significant differences for the item “When I use ICTs, I am more curious during learning,” χ²(5) = 15.505, p = 0.008. Post hoc testing showed TEES students (M = 3.04, SD = 1.27) believed this less strongly than did NSMI students (M = 3.29, SD = 1.07), SSBAL students (M = 3.39, SD = 1.05), and HW students (M = 3.44, SD = 1.12). And AH students (M = 3.20, SD = 1.33) believed this less strongly than did HW students (M = 3.44, SD = 1.12). Istenič Starčič & Lebeničnik 78 Table 9. ANOVA Results for Student’s Beliefs About ICT Use. Items Study field - KLASIUS-P M SD Betweengroup difference (Post hoc test /pairwise comparison) BEL1. Using ICTs for learning allows me to customize the learning process to my needs. χ² = 11.008, p = 0.051 TEES 3.49 0.96 n.s AH 3.49 1.24 SSBAL 3.70 1.00 NSMI 3.64 1.01 EMC 3.50 0.97 HW 3.68 0.93 Total 3.60 1.01 BEL2. Learning with ICTs is more fun than traditional learning. χ² = 20.880, p = 0.001 TEES 3.10 1.10 TEES > SSBAL (p = 0.013, d = 0.66) TEES < HW (p = 0.004, d = 0.25) AH > SSBAL (p = 0.001, d = 0.55) AH < HW (p = 0.001, d = 0.29) EMC > SSBAL (p = 0.037, d = 0.77) EMC < HW (p = 0.010, d = 0.21) NSMI < HW (p = 0.027, d = 0.15) AH 3.03 1.30 SSBAL 2.36 1.13 NSMI 3.22 1.21 EMC 3.17 0.96 HW 3.41 1.29 Total 3.22 1.08 BEL3. I have better grades because of use of ICTs for learning. F(5) = 21.137, p = 0.001 TEES 2.84 1.12 TEES < HW (p = 0.040, d = 0.23) TEES < EMC (p = 0.020, d = 0.25) TEES < SSBAL (p = 0.002, d = 0.30) TEES < NSMI (p = 0.001, d = 0.32) AH < SSBAL (p = 0.006, d = 0.22) AH < NSMI (p = 0.001, d = 0.25) AH 2.91 1.24 SSBAL 3.17 1.01 NSMI 3.22 1.21 EMC 3.12 1.13 HW 3.10 1.07 Total 3.09 1.07 BEL4. ICT use allows me to be more creative in learning. F(5) = 5.558, p = 0.352 TEES 3.11 1.08 n.s AH 3.17 1.12 SSBAL 3.27 0.99 NSMI 3.12 1.09 EMC 3.15 0.99 HW 3.28 1.01 Total 3.16 1.05 BEL5. When I use ICTs, I am more curious during learning. χ² = 15.505, p = 0.008 TTES 3.04 1.27 TEES < NSMI (p = 0.003, d = 0.21) TEES < SSBAL (p = 0.005, d = 0.30) TEES < HW (p = 0.001, d = 0.32) AH < HW (p = 0.035, d = 0.18) AH 3.20 1.33 SSBAL 3.39 1.05 NSMI 3.29 1.07 EMC 3.23 1.10 HW 3.44 1.12 Total 3.49 1.36 BEL 6. ICT use supports me in better collaboration with others. F(5) = 2.146, p = 0.056 TEES 3.47 1.04 n.s AH 3.43 1.14 SSBAL 3.70 0.93 NSMI 3.55 0.99 EMC 3.56 0.97 HW 3.59 0.92 Total 3.55 1.00 Students’ Perceptions of Educators as Role Models 79 Table 9. ANOVA Results for Student’s Beliefs About ICT Use. (continued) BEL7. ICTs allow me to learn anywhere. χ² = 8.086, p = 0.152 TEES 3.64 1.29 n.s AH 3.50 1.42 SSBAL 3.77 1.20 NSMI 3.61 1.31 EMC 3.57 1.28 HW 3.68 1.21 Total 3.63 1.13 BEL8. Others (e.g., professors, colleagues) can see positive results, when I can use ICTs for learning. χ² = 10.628, p = 0.059 TEES 2.61 0.89 n.s AH 2.69 1.02 SSBAL 2.81 1.02 NSMI 2.77 1.00 EMC 2.72 1.04 HW 2.57 1.08 Total 2.71 1.00 BEL9. Information gathered online is better than information from other sources. χ² = 29.704, p = 0.000 TEES 2.50 0.75 AH < EMC (p = 0.019, d = 0.34) AH < NSMI (p = 0.001, d = 0.46) AH < HW (p = 0.001, d = 0.67) AH < SSBAL (p = 0.001, d = 0.55) TEES < SSBAL (p = 0.003, d = 0.29) ECM < SSBAL (p = 0.008, d = 0.26) NSMI > SSBAL (p = 0.024, d = 0.21) AH 2.23 0.96 SSBAL 2.75 0.91 NSMI 2.59 0.91 EMC 2.53 0.76 HW 2.86 0.92 Total 2.57 0.94 BEL10. On the Web, I have access to learning information I could not get anywhere else. F(5) = 10.724, p = 0.057 TEES 3.59 1.11 n.s AH 3.72 1.07 SSBAL 3.85 0.96 NSMI 3.80 1.03 EMC 3.66 0.98 HW 3.78 1.08 Total 3.75 1.01 BEL11. ICT use offers me a feeling of belonging to a group. F(5) = 8.027, p = 0.155 TEES 2.56 1.01 n.s AH 2.46 1.11 SSBAL 2.67 1.09 NSMI 2.52 1.11 EMC 2.47 1.08 HW 2.63 1.15 Total 2.55 1.09 BEL12. Using the Web for learning, I can get access to more information than with any other source (e.g., books, professors). χ² = 38.359, p = 0.000 TEES 2.95 1.29 AH < EMC (p = 0.004, d = 0.28) AH < SSBAL (p = 0.001, d = 0.38) AH < NSMI (p = 0.001, d = 0.28) AH < HW (p = 0.001, d = 0.38) TEES < EMC (p = 0.031, d = 0.29) TEES < SSBAL (p = 0.001, d = 0.33) TEES < NSMI (p = 0.001, d = 0.23) TEES < HW (p = 0.001, d = 0.33) AH 2.89 1.29 SSBAL 3.35 1.12 NSMI 3.24 1.18 EMC 3.22 1.05 HW 3.36 1.15 Total 3.22 1.09 Istenič Starčič & Lebeničnik 86 The present study identifies teachers as role models, highlighting their leading role in ICT use and advancing the professional use of ICTs. TEES students identified teacher-educators as currently not playing the role of leaders in bringing technological innovation into the classroom or curriculum. Students’ beliefs about ICT use show that TEES students have less strong beliefs about ICTs supporting interaction and devising online information. This is in line with the deficiency identified in social network practices, discussing in social networks in the study field, posting blogs, deploying recommendation systems, and co-creating documents, when compared to students in other disciplines. Based on findings, we identified a need for teacher-educators addressing rapidly developing technological innovation and its efficiency for interaction in learning process and learning environment design. In the future, these needs, among many, require broader examination as a foundation for preparing future educational professionals for ongoing work with technology and for technology use in lifelong learning. In a technologically rapidly developing society, teachereducators must design their teaching practices in the present for their students’ benefit in the future, and thus must critically address the constantly evolving technology. In line with related studies, the findings of the present study establish the difference in ICT implementation in teaching and learning among higher education professional disciplines (Croteau et al., 2015). So although this paper has focused on the implications of our findings for students in the education discipline, broader and deeper exploration other disciplines regarding these issues are needed in the future. IMPLICATIONS FOR RESEARCH AND APPLICATION The findings of this exploratory study raise questions about instructional design in all disciplines when integrating a guided learning environment, which is imposed by a university, with the unstructured learning environment driven by student planning. From a research perspective, this study contributes to the limited current findings regarding students’ perceptions of technology integration as useful for their current learning and future professional applications. Thus, it provides a basis for larger studies of this nature and for deeper investigation into the myriad issues related to the triad of technology-enhanced learning—from the universities’ directives, to the teachers’ implementations of new ICT-based pedagogical practices, to the students’ embracing and encouraging effective and integrated use of technologies that will prepare them better for their transitions to ICT-infused professional environments. It is especially important to look at authentic ICT use, which enhances teaching and learning in and of itself as well as develops students’ subject-specific professional competences. With students and teacher-educators in the education field as a focus in this study, our research provides evidence that more study of the specific formation of future teachers is essential for creating environments and academic structures aimed at the influences on future learning. Even now, however, instructional design must provide student teachers with opportunities for selecting and making decisions in creating their personal digital learning environments and networks. This emphasis on studying and developing the ICT-based skills for tomorrow’s teaching professionals rests particularly on social media, activities widely engaged by the current young generation of students. Teachers’ social media use and competences should be examined more closely. Moreover, current and future teachers need support and data to address the Students’ Perceptions of Educators as Role Models 87 challenges raised by the digitalization of childhood, in terms of both the learning potential of students’ familiarity with specific ICTs and the consequent problems and educational impact caused by compulsive use of social media. From the application perspective, the results of our study clearly indicate that some changes already can be implemented in ICT-based higher education. At the university level, planning for the digitalized curriculum requires sufficient time, training, and investment in a larger variety of learning methods, learning technologies, and collaborative activities for both teachers and students. For the teachers, tools and research already exist on ways they can design their current and future curricula and pedagogical decisions to address students’ creativity and engagement in collaborative work with technology to raise their capabilities for being autonomous, selfdirective, and self-efficient in their workplaces. Teachers also can immediately take on the clear understanding that they, as professionals, are role models for ICT use and for the ongoing process of lifelong learning of new processes and technologies. On the other hand, students need to develop not only an understanding of the various ways ICTs can enhance their current studies and future work-related activities, but also take an active role in underscoring the need for these with both university administrators and in-class educators. In that way, students serve as cocreators—or at least motivators—of flexible and responsive curricula for their fields of study. 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Washington, DC, USA: IEEE Computer Society. https://doi.org/10.1109/HICSS.2014.22 Authors’ Note The authors of the paper designed a survey as part of a larger study, “The Online Learning Resources in Higher Education,” supported by Slovenian Research Agency (ARRS) fund for young doctoral researchers. Andreja Istenič Starčič was nominated as a mentor in 2011 (No. 6316-3/2011-784) and her work also is financially supported by Slovenian Research Agency (P2-0210). Maja Lebeničnik was selected as a young researcher in 2012 (No. 2158). In 2017, the authors were invited to conduct a survey within the University of Ljubljana’s project “The Integration of Information and Communication Technology in the Higher Education Pedagogical Process.” All correspondence should be addressed to Andreja Istenič Starčič University of Primorska, Faculty of Education Cankarjeva 5 6000 Koper, Slovenia andreja.starcic [at] gmail.com Human Technology ISSN 1795-6889 www.humantechnology.jyu.fi