What Are Guided Learning Environments

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• Published: 21 May 2019

Investigating cocky-directed learning and applied science readiness in blending learning environment

International Periodical of Educational Technology in College Education volume 16, Commodity number:17 (2019) Cite this article

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Abstract

Blended Learning (BL) creates a 'rich' educational surroundings with multiple engineering-enabled communication forms in both confront-to-face and online educational activity. Students' characteristics are closely related to the learning effectiveness in the BL surround. Students' ability to direct themselves in learning and to utilise learning technologies tin can affect pupil learning effectiveness. This written report examined the impacts of cocky-directed learning, technology readiness, and learning motivation on the three presences (social, educational activity, cerebral) amidst students undertaking subjects in BL and non-BL (NBL) settings. The results indicated that the BL environment provides good facilitation for students' social involvement in the course. Student technology readiness plays a stronger function in impacting the teaching presence in a BL environs than NBL surroundings. These findings imply that a proper BL setting creates a cohesive community and enhances collaborations between students. Prior training of learning technologies can potentially enhance students' education presence.

Highlights

• Composite Learning (BL) has been advocated in college education section.

• This study investigates the impacts of self-directed learning, technology readiness, and learning motivation on students' perception of three presences (social, teaching, cognitive).

• Results evidence that students in the BL grouping achieve significantly higher social presence than students in the NBL grouping.

• Cocky-directed learning has significant and directly impacts on the cognitive presence of students in the BL setting.

• Student applied science readiness plays a stronger role in impacting the teaching presence in BL environment than NBL surround.

• Social presence has significant impacts on the other two presences.

Introduction

Composite Learning (BL) creates a rich educational surround enabling various forms of communication by combining face-to-face learning with technologically enhanced learning so that instruction and learning occur both in the classroom and online (Collis & Moonen, 2012). A blended learning course lies between a continuum anchored at contrary ends by entirely contiguous and fully online learning environments (Rovai & Jordan, 2004). The constructive integration of the contiguous and Internet Technology (Information technology) components determines the quality of course design so that blended learning is non just an add together-on to the existing dominant arroyo or method (Garrison & Kanuka, 2004). In the higher education context, the interaction and sense of appointment in a community provide the atmospheric condition for free and open up dialogue, disquisitional debate, negotiation and understanding, which are the authentication of effective teaching (Garrison & Cleveland-Innes, 2005). The Community of Enquiry (CoI) framework is widely used in online learning inquiry and pedagogy for enriching students' learning feel (Annand, 2011). The three presences in the CoI framework, social presence, cognitive presence, and teaching presence integrally promote social and intellectual interactions among participants and materials and, thereby, fruitful learning outcomes (Annand, 2011; Garrison, Anderson, & Archer, 2000). The iii presences likewise offer a user-friendly instrument with three dimensions to assess the students' perceptions of the learning experience and reflect their learning effectiveness.

In the online learning scenarios, where the structure of an online curriculum is mostly automatic (Khan, 2009), students take more flexibility in deciding when, how and with what content and activities they engage (Milligan & Littlejohn, 2014). This flexibility requires students to monitor and adjust their behaviour and actions concerning the specific learning context (Zimmerman, 2000). Students are aware of their learning responsibleness in themselves instead of an external source, such as a teacher (Demir, 2015). A self-directed learner tends to actively engage in the learning processes, such as acquiring information, planning and evaluating the learning activities. Agile learning strategies can increase students' participation and improve the learning procedure and operation (Freeman et al., 2014; Yilmaz, 2016). Still, non much empirical evidence is available in the extent literature regarding the bear on of self-straight learning in the composite learning setting.

Engineering readiness is another critical dimension connected with students' learning in the blended learning environment. The emergence of various computer technologies enables the usage of multimedia content and multimedia communication (Horton, 2006) for educational activity, and provides anywhere, anytime access to the learning content. Existing studies have been focused on students' adoption of learning technologies and the determinant factors, for example, personal innovation, perceived usefulness, performance expectancy, endeavor expectancy, social influence, perceived playfulness, cocky-direction of learning, using the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and User of Technology (UTAUT) (Liu, Li, & Carlsson, 2010; Wang, Wu, & Wang, 2009). Students' engineering science readiness refers to their propensity to embrace new technologies for accomplishing goals in learning (Parasuraman, 2000). Studies on eastward-learning readiness constitute that students' level of eastward-learning readiness can influence of level of success in e-learning (Moftakhari, 2013; Piskurich, 2003). Today, most academy students are digital natives and use technology well (Prensky, 2001). Notwithstanding, the utilisation of learning technologies combined with traditional in-class teaching is still a developing teaching approach for university instructors and students, and predictors of learning effectiveness remain unclear (Hao, 2016).

Too, though at that place are some inquiry works on technology enhanced learning, in that location exists no well agreed results. Studies have shown different results which contains positive relationship, negative relationships, and no significant relationships between using the internet for course material and educatee learning issue (Gulek & Demirtas, 2005; Shouping & Kuh, 2001; Sana, Weston, & Cepeda, 2013).

To make full this gap, it necessary to explore the impact of technology readiness and individual behaviour on bookish performance in the blended learning context. While motivation is one of the success factors for online learning (Lim, 2004), for its significant impact on learner attitudes and learning behaviour in traditional educational environments (Fairchild, Jeanne-Horst, Finney, & Barron, 2005).

The purpose of this newspaper is two-folded. Outset, grounded on the Theory of Planned Behavior (TPB) which posits individual behaviour is driven by behaviour intentions, and social cognitive theory, this written report explores the deeper connections between self-directed learning, technology readiness, and educatee motivation, to understand their integral effects on the three presences of CoI (teaching, cerebral and social), thus expanding the literature in blended learning research and examining its influencing factors which accept not been sufficiently explored.

2d, considering the lack of studies addressing the interdependencies in different settings, our study compares the interdependences in blended learning and non-composite learning, with the aim to provide empirical evidence and insights for instructors to adopt a proper instructional strategy in online, and offline teaching.

The subsequent sections of this paper discuss the related literature supporting the proposed research model. The inquiry hypotheses and data collection method are then presented. The results and findings are reported, and conclusions are drawn.

Literature review

Composite learning

Blended Learning (BL) integrates face up-to-confront learning with online learning and enables asynchronous teaching and learning (Graham, 2013). Littlejohn and Pegler (2007) used "strong" and "weak" blends to indicate the various amounts of east-learning. Through a diversity of online learning technologies, such as online word forums, BL enables communication amidst learners and between learners and teachers. Effective integration of traditional classroom teaching with e-learning provides support to asynchronous and cooperative learning among students. Achieving a balance between classroom and online learning is necessary every bit students still value the face-to-face opportunities to receive feedback in BL setting (Vanslambrouck, Zhu, Lombaerts, Philipsen, & Tondeur, 2018).

There have been a number of studies carried out on the adoption of effective educational engineering (Findik & Ozkan, 2013; Mtebe & Raisamo, 2014). Graham et al. (2013) identifies that strategy, construction, and support are three key factors for BL adoption. Challenges to the design of effective BL course accept been classified by Boelens, De Wever, and Voet (2017) into four types, which includes incorporating didactics flexibility, facilitating students' interaction, facilitating learning process, and fostering affective learning climate. Porter, Graham, Actual, and Sandberg (2016) constitute that innovation adoption strategy affects how institutional strategy, structure, and back up decisions facilitate or impede BL adoption. Also innovation adoption strategy, institution blocks, teachers and students also determine the key factors promoting successful BL. Institutional blocks including organisational readiness, adjacent technical resources, motivated kinesthesia, professional development for teachers and students' maturity and readiness for blending learning are all concerns (Tabor, 2007; Vaughan, 2007). Therefore, the learning experience of students in BL courses is presumed to exist influenced by a different set of factors from traditional classes.

Theory of planned behaviour and social cognitive theory

The Theory of Planned Beliefs (TPB) argues that individual attitude toward the behaviour tin determine individual behaviour (Ajzen, 1991). Social Cognitive Theory (SCT) explains homo behaviour through three interacting determinants: cognitive, melancholia and biological events; environment, and behaviour (Compeau & Higgins, 1995). TPB and SCO are widely applied in studies to explain private behaviour related to technology use (Barnard-Bark, Burley, & Crooks, 2010; Compeau & Higgins, 1995).

Personal factors such every bit own intentions and attitudes are the chief focus of this study. Students' self-directed learning here refers to students' perceptions of their independent learning, their sense of responsibleness in their learning and their initiative in learning. Self-directed learning shares some common features with self-regulated learning. Broadbent (2017) found that self-regulated learning has dissimilar predictive value among online learners and BL learners. Technology readiness refers to individual attitudes toward new technologies. Students' perceptions in CoI to a certain extent reverberate the learning effectiveness or learning experience of students in a course. Based on TPB and SCT, cocky-directed learning and technology readiness are postulated to be able to differently drive the students' learning behaviour, with dissimilar learning experience and perceptions of CoI.

Community of enquiry

A community is essential to support collaborative learning. The framework of the Community of Inquiry (CoI) developed by Garrison et al. (2000) provides necessary guidance for the employment of instructional technologies to support the BL environment. There are three dimensions in CoI framework, which include social presence, cognitive presence, and didactics presence. Social presence represents the ability of learners to behave socially and emotionally. The student group cohesiveness and interaction is strongly correlated with the learning outcomes (Hwang & Arbaugh, 2006; Williams, Duray, & Reddy, 2006), which are essential in a BL design. Cognitive presence refers to the extent that learners can absorb meaning in the process of reflection and discourse. Cognitive presence involves applied inquiry (Garrison & Arbaugh, 2007), interaction and critical thinking skills of the participants (Duphorne & Gunawardena, 2005). Didactics presence refers to the design, facilitation and management of student learning and thinking processes (Garrison et al., 2000). Students' sense learning community and satisfaction are influenced by pedagogical design of BL course (Shea, Li, Swan, & Pickett, 2005), and particularly education presence (Arbaugh, 2007).

These three presences are closely interconnected (Akyol & Garrison, 2008; Shea et al., 2010). Teaching presence makes the educatee become more actively thinking almost the learning content and involvement in student learning word, thus improves cognitive and social presences (Ke, 2010). Social presence tin can besides predict pupil cognitive presence (Archibald, 2010). Still, the interrelationships are dynamic in a unlike learning setting betwixt the three presences and requires further exploration.

Self-directed learning and learning effectiveness

Cocky-directed learning (SDL) refers to the psychological processes of learners that purposively directly themselves to proceeds knowledge and empathize how to solve bug (Long, 1994). Self-directed learners usually more actively participate in learning tasks such equally reading online learning cloth, completing classroom tasks, planning and evaluating milestones of learning. High-level cocky-management is important in SDL and learners to need to adopted different strategies in dealing with diverse problems (Lee & Teo, 2010). Similar to self-regulated learning, SDL too emphasises on goal setting and choice making, which are crucial to student collaborative learning (Gilbert & Driscoll, 2002). The difference betwixt SDL and cocky-regulated learning lays in their required skills. The constructs of SDL are at the macro level, and constructs of self-regulated learning belong to micro-level (Jossberger, Brand-Gruwel, Boshuizen, & Wiel, 2010).

Cocky-directed learners tend to search the online learning platform for resources. Enquiry on self-directed learning with technology (SDLT) (Teo et al., 2010) revealed that students' perceptions of collaborative learning can enhances students SDL. Student SDL processes contribute to the use of Internet communication technology for collaborative learning (Lee, Tsai, Chai, & Koh, 2014). The role of self-regulated learning discussed in the CoI framework was found to be positively related to students' perceived CoI and affective outcomes (Cho, Kim, & Choi, 2017; Garrison & Akyol, 2015). Learners that are skilled at SRL will visit course materials more than oftentimes (Kizilcec, Pérez-Sanagustín, & Maldonado, 2017). Despite that existing studies reveals of impact of SDL on learning effectiveness, how SDL enhances or undermine students' perception of CoI remains unexplored.

Technology readiness and learning effectiveness

Applied science-readiness refers to one's willingness to leverage new technologies in performing tasks (Parasuraman, 2000). Web-based technologies, though well established, still face the challenge of being readily accepted when introduced to a new awarding setting. Compared to traditional classroom learning, students' readiness to accept and employ web-based learning resource varies across individuals. Students' attitude toward engineering-based applications reflects their applied science readiness in the learning scenarios. Cheon, Lee, Crooks, and Song (2012) found that college students' attitude positively influences their intention to adopt mobile learning. For the composite learning context, using online learning sources is compulsory. Otherwise, information technology will be not possible to get the desired learning consequence.

Equally mentioned earlier, the utilise of learning technologies has dissimilar impacts on students' learning outcomes which may exist caused past contextual and cognitive factors (Hong, Hwang, Liu, & Chen, 2014). BL environment was found to increment educatee attendance and learning satisfaction in science education (Stockwell, Stockwell, Cennamo, & Jiang, 2015). Moreover, using online class material can heighten student intellectual development (Shouping & Kuh, 2001). On the other hand, some students reported that their course grades decrease as they spend too much time on online form fabric. These diverse inquiry results reveal the involvement and importance of exploring the readiness for learning technologies and its influences on students' perceptions and behaviours. Parasuraman (2000) adult and validated a measurement scale, called the Engineering Readiness Alphabetize (TRI) for applied science readiness, which consisted of 28 items, clustered into iv categories: optimism, innovativeness, discomfort, and insecurity. These four categories integrally reflect the individual attitude toward new technologies in the learning procedure.

Learning motivation

Learning motivation is the process whereby goal-directed activity is instigated and sustained, and it is reflected in personal investment and in cognitive, emotional, and behavioural engagement in learning activities (Fredricks, Blumenfeld, & Paris, 2004). Inquiry on students' learning reveals that self-efficacy and goal settings are highly related to learning motivation (Che-Ha, Mavondo, & Mohd-Said, 2014; Law & Breznik, 2017; Law, Lee, & Yu, 2010; Ngan & Constabulary, 2015). Motivation is an essential gene in the completion of both online and in-class learning activities. Although diverse educational research emphasises on learning motivation, its relationships betwixt cocky-directed learning and applied science readiness have non been sufficiently explored in the blended learning setting.

Research questions

The BL surroundings offers a dissimilar setting with multiple media for teaching, communication, word and evaluation. The show from the existing literature highlights the importance of cocky-monitored learning behaviours and engineering science readiness in the online learning environment. The balance between online learning and in-class learning is relatively hard to attain and i of the challenges for BL course design. Exploring the impacts of self-directed learning and technological readiness on students' motivation and perceptions of CoI can deepen the agreement of composite learning grade pedagogy design. The comparison between composite learning and non-blended learning grade students tin further provide insights into special needs and behaviours of students in a composite learning surroundings. Therefore, the enquiry questions of this study are:

• Q1. Is there difference between students attending BL courses and student attending traditional classroom form in their perception of CoI?

• Q2. Practice self-directed learning and technology readiness have equal influences on students' motivation and perception of CoI between BL and traditional classroom class?

Methodology

Research hypotheses

A concept model is proposed, as illustrated in Fig. 1, which presents hypothesised relationships between self-directed learning, engineering readiness, motivation and students' perceptions of CoI. The conceptual model is also practical to examine the differences of interrelationships betwixt BL student groups and not-BL student groups. In this section, hypotheses are developed in response to our inquiry questions.

Fig. one

Conceptual structural model

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Impacts of cocky-directed learning

Self-directed learners actively engage in the learning process and can adopt proper learning strategies according to the learning setting. A technology-rich learning environment tin provide students with bully opportunities and abilities to be self-directed in their learning (Fahnoe & Mishra, 2013). The composite learning teaching context offers students opportunities to interact with instructors and classmates face-to-face through discussion and self-controlled access to multimedia learning content. Cocky-directed aspects of learning (the choice of what, when, and how long to study) take significant repercussions in the effectiveness of users' learning efforts (Tullis & Benjamin, 2011). Facing uncertainties in the online learning context, students need to adjust or formulate their own best learning strategies. It is anticipated that highly self-directed learners are involved in learning activities online more actively by asking questions and joining in discussions, thus have a stronger sense of CoI than students with low self-directedness. Cocky-directed students besides have a stronger willingness to attain learning goals. Therefore, we put forward the following hypotheses:

• H1.Student self-directed learning readiness positively correlates with students' perception of CoI

• H2. Self-directly learning positively correlates with learning motivation

Impacts of engineering readiness

Students with higher levels of technology readiness hold a positive attitude toward technological learning media and innovative platforms for communication. Students with a sense of discomfort and insecurity in adopting technologies may take a longer fourth dimension to become efficient users of online learning platforms. The blended learning context requires students to complete the online learning tasks together with in-course learning activities. Student factors such as self-efficacy in using the figurer, motivation toward t-learning are efficient to fulfil the online course prerequisites, (Demir, 2015; Hao, 2016; Moftakhari, 2013). Previous studies have evaluated students' readiness for specific learning technologies or platforms (Cheon et al., 2012; Shouping & Kuh, 2001). Students' mental attitude toward the broad collection of new technology products includes optimism, innovativeness, discomfort, and insecurity (Parasuraman & Colby, 2015). Students with optimism and innovativeness toward learning technologies are more willing to prefer the online learning strategy than students with discomfort and insecurity. Therefore, we put forrard following these hypotheses:

• H3. Student Technology readiness positively correlates with students' perception of CoI

• H4. Technology readiness positively correlates with learning motivation

Impacts of learning motivation

It is believed learning motivation tin can influence students' attitudes and behaviour in educational environments (Fairchild et al., 2005). In the online learning context, strongly motivated students are more likely to watch videos and read the online learning material compared to students who are less motivated. Thus, motivation is mainly related to student learning effectiveness in the blended learning setting. Therefore, our fifth hypothesis is:

• H5. Student learning motivation correlates with students' perception of CoI

Relationships between three presences

Students, who behave more socially and emotionally in mediated communication, can interact with group members more than efficiently, thus enhance the group cohesiveness. Socially advice can also facilitate the communications between teachers, platforms and students. In the interactions, students tin can develop critical thinking skills to deal with various types of opinions and reflect on the learning content. Therefore, nosotros hypothesise that:

• H6. Social presence positively correlates with cognitive and teaching occurrences

  • H6a. Social presence positively correlates with teaching presence

  • H6b. Social presence positively correlates with the cognitive presence

Blended learning context for applied science students

Diverse BL models have been reported to be useful in previous studies (Dziuban & Moskal, 2001; Martyn, 2003). Blended learning undergraduate courses were designed for technology students in a university in Hong Kong. Engineering students are expected to be more than adaptive to practical situations co-ordinate to their abilities (Police force & Geng, 2018). Individual differences in these learning attributes pose challenges to engineering education whose aim is to provide instruction most "real-earth" engineering science design and operations, provide grooming in disquisitional and creative thinking skills, provide graduates who are conversant with technology ethics and connect between applied science and order (Felder, Woods, Stice, & Rugarcia, 2000). The engineering management subjects, including innovation and entrepreneurship, and the business procedure are adult with a mixture of collaborative learning, project-based learning (PBL), team/peer learning, and contained learning. An online learning direction system (LMS), as shown in Fig. 2, was adopted and integrated with face-to-face in-class teaching. The primary learning objectives of these engineering management subjects are to fix technology students with basic agreement of engineering direction concepts and the relevant techniques, tools and skills, while the application of knowledge and team skills are likewise emphasised. The LMS offers students online learning materials, and chapter-end exercises according to the predefined course outline. Students tin, therefore, learn at their own pace. Videos of real-life example studies are also available on the LMS, to provide students with further elaboration on the learnt concepts.

Fig. 2

Interface of the LMS used for the blended learning subject

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Elements in this course, include classroom instruction, E-learning, workshop in class (the practice of learnt cognition, and peer learning on specific topics such application of assessing and analytical techniques), group projects (peer learning, use of knowledge, sharing of feel, and reflective learning). This organization of blended components in the grade is to keep students motivated and on-track, while they can learn interactively (interactivity) in the classroom, collaboratively in workshops and in grouping projects with peers. They would also exist able to develop proficient communication with both peers and teachers in person, or via diverse reflections. An agreed assessment plan also shows the learning progress of the students, likewise as to pinpoint the areas for comeback in the learning journeying. To summarise, the assessment consists of Individual assignments, tests and in-class activities (fifty%), Group, peer learning projects (thirty%), Reflections on individual and peer learning (20%).

Data drove

Instrument

A questionnaire was developed corresponding to the factors in our model (see Fig. 1), using 5-betoken Likert scales (1 = strongly disagree; v = strongly agree). The questionnaire has ii parts, and the offset office contains four scales from existing studies:

1. 1)

   The learning motivation calibration was used in Law and Geng (2018) on student innovativeness and handedness.

2. 2)

   Self-directed learning with applied science (Lee et al., 2014; Teo et al., 2010) for measuring young students' perceptions of cocky-directed learning with the back up of technology.

3. 3)

   Technological readiness alphabetize (Parasuraman, 2000; Parasuraman & Colby, 2015) measures people's propensity to embrace and apply new technologies in four dimensions: optimism, innovativeness, discomfort, and insecurity.

4. 4)

   Modified CoI instrument consists of education presence, social presence, and cognitive presence (Arbaugh et al., 2008).

The second part consists of the personal particulars of respondents, such every bit gender, historic period, discipline, and year of study. A pilot study was carried amidst ten volunteer students, to ostend the validity of the questionnaire, earlier data collection. For the data collection, we invited voluntary participation from students who were in the form. The data drove was carried out most the end of the semester. There were 96 valid samples received from the Blended Learning (BL) student group out of 102 responses and 111 valid samples received from Non-BL (NBL) student group out of 121 responses.

Measurement model interpretation

Fractional Least Squares (PLS) (Henseler & Sarstedt, 2013) was adopted to approximate the proposed model (Fig. 1). The unidimensionality of half dozen blocks of constructs (learning motivation, cocky-directed learning readiness, engineering science readiness, social presence, cognitive presence, instruction presence) and the results contained in the outer model were firstly tested. Cronbach's alpha, Dillon-Goldsteim's rho, Composite reliability and AVE were used to check the unidimensionality.

As presented in Table 1, the Cronbach'southward blastoff, Dillon-Goldsteim'due south rho, Composite reliability for all the constructs are above 0.seventy (Sanchez, 2013). The AVE values are to a higher place the threshold of 0.fifty (Fornell & Larcker, 1981) among all the constructs. Therefore, the construct validity of the measurements fulfil the requirement.

Table one Unidimensionality of constructs

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The outer weights, loadings and communality measures shown in Table 2 demonstrate the convergent validity as particular loadings are higher than threshold (Factor loadings> 0.7, communalities > 0.5). The discriminant validity condition was also fulfilled every bit square root of the AVE for each construct is larger than its correlation with other construct every bit shown in Table 3 (Chin, 1998; Fornell & Larcker, 1981).

Table ii The outer model estimation

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Tabular array 3 Correlation betwixt constructs

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Results

Pupil demographics

A full of 102 engineering students participating in BL courses and 121 technology students participating in non-BL courses filled in the questionnaire and provided a total of 207 valid answers. An overview of these participants is presented in Tabular array four.

Table iv Demographic details of respondents

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The results obtained using One-way ANOVA indicate that gender, yr and age of the students do non influence the results of six constructs. Therefore, participants were treated equally a unmarried grouping in our assay.

Difference between BL and NBL groups

The measurement item mean scores for learning motivation (LM), self-directed learning (SDL), technology readiness (TRD), social presence (SP), cognitive presence (CP), and teaching presence (TP), and their standard divergence among all participating students in both BL and NBL group are presented in Table 5.

Tabular array 5 Overall statistical results

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As seen from Tabular array 5, the BL group students accept higher mean scores for LM, TP, SP than the NBL group. The NBL group has higher mean scores for SDL and CP than the BL grouping. However, the difference between the mean scores of the BL and NBL groups are minimal. Thus nosotros performed statistical analysis to exam the significance of the difference. Independent sample t-testing at a significance level of 0.05 was carried out. The results obtained are shown in Tabular array 6.

Table 6 BL grouping and NBL group mean comparing

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The t-exam results show that students participating in BL courses have significantly higher levels of social presence than students attending not-BL classes (p < 0.010). This result supports our hypothesis H1a. Students from NBL groups show higher levels of technology readiness than students from the BL group (p < 0.050). No significant differences were found for cognitive and instruction presences between the BL and NBL student groups. Thus hypotheses H1b and H1c are non supported.

PLS modelling results

PLS modelling was carried out amongst the BL group students and the NBL group students separately. The human relationship between learning attributes is graphically presented in Fig. 3a and b. The statistical testing results are reported in Tables vii and 8. All path coefficients between the latent variables in the models are positive, which bespeak the positive relationships betwixt each pair of connected factors. Both direct and indirect relationships are examined in the structural model, and the results are likewise included in Tables vii and 8. Although PLS path modelling does not provide a widely adequate global model fit (Chin, 1998; Sarstedt, Ringle, & Gudergan, 2017), we tin can still assess the model fit past using the Standardized Root Mean Foursquare Residuum (SRMR) and Chi-square methods to the degree of freedom (x²/df). If SRMR value is less than 0.x or 0.08, the model fitness is considered as skilful (Sarstedt et al., 2017). If 10²/df is less than 5 and larger than 2 when the sample size is larger than 200, the modelling result is considered to be satisfactory (Hafiz & Shaari, 2013). For both structure models (BL and NBL) in this study, the SRMR values are less than 0.08 (BL_SRMR = 0.066, NBL_SRMR = 0.079). The x²/df values are also within satisfactory range (NBL_ ten^two/df =2.385, BL_ x^two/df =iv.246).

Fig. three

a PLS result for engineering students in the BL grouping. b PLS result for engineering students in the NBL group

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Table 7 Structural path coefficients for the BL grouping

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Table 8 Structural path coefficients for NBL group

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Justification of the hypotheses

An overview of the statistical examination results of the hypothesised relationships is presented in Table ix. The different results between the BL and NBL student groups are highlighted.

Tabular array 9 Hypotheses testing results

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Discussion

The structural models of the BL and NBL groups reveal different patterns of interrelationships between the learning attributes and the iii presences. Both modelling results highlight the critical roles of self-directed learning, engineering readiness, and learning motivation in influencing the learning effectiveness in both BL and NBL settings, and imply how the BL setting can be farther modularised for diverse themes and educational purposes.

The three presences in BL settings compared to NBL settings

Students in the BL group achieve significantly higher social presence than students in the NBL grouping. This result indicates that the BL setting surpassed traditional contiguous educational activity setting in socially involving students. The BL course setting provides an open communication environment for students, which allows the students to express themselves socially and emotionally through communication (Garrison et al., 2000). Students tin interact with each other and with teachers through online learning platforms besides traditional contiguous discussion. Social presence provides the cohesion to sustain students' participation and focus. Information technology also creates a sense of belonging, supporting freedom of expression. Therefore, a proper BL setting creates a cohesive customs and enhances collaborations betwixt students. The results also support that students in blended courses have higher levels of 'sense of customs' than complete online course (Rovai & Hashemite kingdom of jordan, 2004). The BL setting offers more than all-rounded learning facilitation to assist with students' dissimilar learning scenarios.

From the results of our study, social presence positively enhances educational activity presence and cognitive presence, as shown in the structural models (Fig. 3a and b), confirming the close interrelationships amid the presences (Akyol & Garrison, 2008; Garrison Cleveland-Innes, & Fung, 2010; Shea et al., 2010). Social presence is institute to have a directly issue on the cerebral presence (Shea & Bidjerano, 2009), whereas educational activity presence does not have a direct relationship with the cognitive presence in the BL setting. Cognitive presence allows students to take reverberate on their interpretations (Garrison et al., 2000). The communication among student grouping members during collaborative activities contribute to students' systematic and disquisitional thinking, which is the hallmark of effective college didactics. Instructor expertise, instructor support, and students' self-efficacy influence student satisfaction (Diep, Zhu, Struyven, & Blieck, 2017). In the BL setting, where instructional technologies are in use, the roles of instructors to organise the class, facilitate the soapbox, direct the cohesion are overwhelmed by the technology-enhanced learning media. This explains the weakened influence of the teaching presence on cognitive presence.

Attributes determining learning effectiveness in BL and NBL settings

Self-directed learning and cognitive presence

Self-directed learning has significant and directly impacts on the cerebral presence of students in the BL setting, while it does not have a directly affect on the cognitive presence in the NBL setting. In the BL setting, students are expected to directly themselves in learning on the online platforms, whereas teachers in the face-to-face NBL setting pb them. Enhancing student power to control and to direct for understanding helps students learn more actively in exploring course content and ideas. The BL setting allows students to construct and confirm pregnant through reflection on their own. In the NBL setting, teachers play the office of directing, explaining, and pace controlling, which makes the learning effectiveness less dependent on student cocky-directed behaviour.

Self-directed learning, technology readiness and learning motivation

Cocky-directed learning and technology readiness have a positive influence on learning motivation in BL, whereas in the NBL learning environs only engineering science readiness influences learning motivation. The results imply that students who are more than self-directed and with agile attitudes toward applied science-based products are more motivated in adopting online learning strategies and achieving their learning goals. In the NBL setting, learning motivation is influenced by technology readiness, but non cocky-directed learning. This implies that spider web-based learning engineering science can be a complementary extension of the traditional classroom teaching for inducing self-directed learning effects which in return, can influence learning motivation. It is therefore meaningful to integrate and optimise online and offline course design to reduce students' difficulty in adopting the learning technologies, with the aim of enhancing student learning motivation.

Learning motivation, didactics presence and social presence

Learning motivation is found positively influencing the social presence in both the BL and NBL teaching environments, where learning motivation represents the personal goal orientation that a student brings to a class of study (Lynch & Dembo, 2004). Students with stronger learning motivation will engage more in the learning process and discuss more with group members for the thought give-and-take and content understanding. This explains the positive influence of learning motivation on instruction presence in both the BL and NBL setting.

Engineering readiness and educational activity presence

Technology readiness plays a more important role in influencing education presence in the BL learning surroundings than the NBL learning surroundings while both are statistically significant. Students' intention to adopt spider web-based learning technologies determines students' attitude to learning behaviour and perceived behavioural control. Students who are readier to adopt the spider web-based learning approach understand the online and offline form design ameliorate and are more than enlightened of teaching presence while teaching presence is critical to the course and facilitation design. Our results, therefore, provide implications that course designers need to consider technology readiness when adopting BL teaching approach, for more effective educational activity presence.

Conclusions and future report

In this report, we investigate the roles of self-directed learning, technology readiness, and student motivation in BL and NBL settings and their impacts on educatee's perception of the three presences from the CoI framework. The results show that the BL environment is better than the NBL environment in providing learning facilitation. The results from structural modelling imply that cocky-directed learning plays a vital function in influencing the cognitive presence, while in the NBL environment it does non. Course designers and instructors shall recognise the value of fostering students' cocky-directed learning in a more flexible learning context. The impact of social presence on the other two presence indicates the importance of emotionally and socially engaging students in the learning process in both online and offline learning scenarios. Applied science readiness has a stronger positive influence on pedagogy presence in the BL setting compared to the NBL setting. Prior training or briefing of learning technologies or platforms would potentially better students' perception of education presence.

Limitation of study

Though the sample size was not that large due to the restricted enrolment number for the BL classes and was only offered to a selected group of students of the same background for the better control of the experiment. Given the above constraints, and with a systematic controlled setting, the sample sizes of 102 and 121 of BL and not-BL students respectively, is considered adequate for providing insights for the specific study.

We expect to extend the study to more selected BL classes further. Due to the resource limit of this study, other types of evidence, for instance, the students' arrangement usage information, are not incorporated here.

Contributions

The findings in our study reveal the impacts of self-directed learning, engineering science readiness and learning motivation on the learning effectiveness in the blended learning environment and the non-blended learning environment. This study expands the literature in blended learning and its influencing factors which have not been sufficiently explored. Past comparison the interdependences in dissimilar learning settings, our study provides empirical evidence and insights for educators for proper instructional strategy adoption in both online and offline teaching, to enhance the perceived social, educational activity, and cognitive presences leading to improved learning outcomes.

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Acknowledgements

The publication of this paper is supported by the Natural Science Foundation of Communist china (Grant Nos. 71571120). Professor Ben Niu is the 2nd corresponding writer of this paper.

Funding

This report is supported past the Natural Science Foundation of Mainland china (Grant Nos. 71571120).

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Almost the authors

Dr. Shuang Geng is currently a Postdoctoral Researcher at School of Direction, Shenzhen University. She obtained her PhD degree at the System Engineering and Engineering Management section of Urban center Academy of Hong Kong. Her research interests include workplace knowledge recommendation, organizational learning, and project direction in the context of Mainland china'due south electronics manufacturing manufacture, designing and developing online learning systems for higher education, and tracking and analyzing educational data. Her research papers appear in "Projection Management Journal", "The Organizational Learning", "Noesis Management: An International Journal", "International Journal of Technology and Pattern Education".

Dr. Kris Police is currently an Acquaintance Professor at the School of Technology, Deakin University, Australia. Prior to her joining Deakin Academy, she was a lecturer at the Section of Industrial and Systems Engineering, Hong Kong Polytechnic University. She currently besides holds a Docentship (offshoot professorship) in the Section of Industrial Technology and Direction, Oulu University in Finland. Her expertise lies in Organizational Learning and Development, Engineering science and Innovation Management, Technology-based Entrepreneurship, Project Direction and Engineering science Education.

Dr. Police force undertook a post-doctoral inquiry scholarship and was a visiting researcher at the Graduate Establish of Industrial Engineering, National Taiwan Academy (2009–2011).

Professor Ben Niu is currently working at Direction Science department at Schoolhouse of Management, Shenzhen University. He used to be Visiting Professor of Arizona State University, Hong Kong University, Hong Kong Polytechnic University, China The Academy of Sciences, Victoria University of Wellington, New Zealand. He has been granted 5 national natural scientific discipline funds, published more than 100 academic papers, and published 3 books. His research interests include big information analysis and processing, learning recommendation systems, entrepreneurship pedagogy, financial engineering and business intelligence, swarm intelligence theory and application, prototype processing, characteristic extraction, artificial intelligence.

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Authors and Affiliations

1. Higher of Management, Shenzhen University, Shenzhen, China

   Shuang Geng & Ben Niu

2. School of Engineering science, Deakin University, Geelong, Australia

   Kris M. Y. Law

Contributions

KMYL carried out the empirical investigation and SG wrote the kickoff draft of the manuscript. KMYL and SG participated in designing the empirical investigation protocol, construction and review the manuscript. BN participated in finalizing the draft. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Kris Thou. Y. Law.

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Geng, Southward., Police force, K.M.Y. & Niu, B. Investigating self-directed learning and technology readiness in blending learning environment. Int J Educ Technol High Educ 16, 17 (2019). https://doi.org/10.1186/s41239-019-0147-0

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• Received: 20 Dec 2018

• Accepted: 25 April 2019

• Published: 21 May 2019

• DOI : https://doi.org/10.1186/s41239-019-0147-0

Keywords

• Blended learning
• Self-directed learning
• Applied science readiness
• Motivation
• Community of inquiry

What Are Guided Learning Environments,

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