Smart utilization of tertiary instructional modes

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Smart utilization of tertiary instructional modes

John Hamilton *, Singwhat Tee 1

James Cook University, P.O. Box 6811, Cairns, QLD 4870, Australia

a r t i c l e i n f o

Article history:Received 10 June 2009Received in revised form 28 September 2009Accepted 13 October 2009

Keywords:Instruction modesPedagogical issuesEvaluation methodologiesImproving classroom teachingInteractive learning environments

a b s t r a c t

This empirical research surveys first year tertiary business students across different campuses regardingtheir perceived views concerning traditional, blended and flexible instructional approaches. A structuralequation modeling approach shows traditional instructional modes deliver lower levels of student-per-ceived learning quality, learning experience and learning skills. A combination of on-line and face-to-facelearning approaches, embedded across each course, yields far higher levels of total learning effects, and toexplain differences in instructional approaches, a ‘Cone of Learning’ continuum is presented and dis-cussed. Theoretical and practical research implications, and the measurement, theoretical and manage-ment aspects of future research options are presented. Tertiary institutions can adopt the approachesherein to assist in the development and build of smart targeted learning solutions – ones more in-linewith the perceived needs of their respective student year levels and groups.

� 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Tertiary educators typically seek to proactively engage students and to add value to their learning experiences (Blankson & Kyei-Blank-son, 2008). In the past, this form of student learning has occurred by traditional, or face-to-face, educator-presented delivery systems. Tra-ditional face-to-face learning approaches have not stayed static, and various direct student learning instructional approaches have emerged(Bonk & Graham, 2005; Gibson & Cohen, 2003; Johnson, Suriya, Yoon, Berrett, & La Fleur, 2002; Michinov & Michinov, 2008; Reisslein, Seel-ing, & Reisslein, 2005).

Students learn by processing and synthesizing information, and they often do so in an individualistic manner (Biggs, 2003; Felder &Spurlin, 2005; Park, 2005; Trigwell & Prosser, 1991, 1997). Student learning is also linked to effective instruction and course design (Biggs,2003; Felder & Spurlin, 2005). Hence, educators who used traditional instruction modes have altered, and enhanced the focus of their learn-ing-related presentations, engagements and instructional materials. For example, PowerPoint supported lectures, study guides, workbooks,may be extended using remote interactive response systems like Qwizdom. Here, the instructor and the student work in harmony, andcontinually learn through high quality, content, and interactions. Problem solving scenarios such as case studies have been added toface-to-face engagement approaches. Such approaches improve classroom interactions and enhance the quality of the learning processes(Bliuc, Goodyear, & Ellis, 2007; Chickering, Gamson, & Barsi, 1987; Johnson & Johnson, 1998, 1999; Simmering, Posey, & Piccoli, 2009).However, the traditional or face-to-face instructional mode still remains within the educator-controlled environment, and within the tea-cher-directed (or tutor-directed) domain (Beattie & James, 1997; Bonk & Graham, 2005; Gamliel & Davidovitz, 2005; Hughes, 2007; McCar-thy & Anderson, 2000; Miller & Groccia, 1997).

2. Learning modes

Under traditional face-to-face instructional mode approaches the student specific-learning and content-related tasks are compliant tothe educator-selected (and educator-directed) allocations (Moore, 1989, 1991; Moore & Kearsley, 2004). These educator-to-student inter-actions generally promote higher order thinking whist also sustaining motivation (Navarro & Shoemaker, 2000).

Today, first year tertiary institution students are often educated in large class size learning situations. This large class size constraint hastypically suited ‘traditional face-to-face student learning approaches. These traditional instructional mode approaches are changing.

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* Corresponding author. Tel.: +61 7 40421091; fax: +61 7 40421474.E-mail addresses: [email protected] (J. Hamilton), [email protected] (S. Tee).

1 Tel.: +61 7 40421494; fax: +61 7 40421474.

Computers & Education 54 (2010) 1036–1053

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Today’s techno-savvy students are often exposed to virtual learning approaches like: Blackboard or WebCT, mobile and PDA interconnec-tion and action devices, remote randomly generated testing banks, podcasts, on-line discussion boards and aids like: blogs, wikis, and socialnetworks (Bonk & Graham, 2005; Dabbagh & Bannan-Ritland, 2005; Hamilton & Tee, 2008). This has changed the traditional student ap-proach into a more interactive and less restrictive ‘blended learning mode’ approach where a mix of face-to-face and virtual learning deliv-ers a combined, and more encompassing, learning environment.

The blended learning mode captures the ‘what’, the ‘where’, and the ‘when’ of learning (Hill, 2006). Blended learning can extend theclassroom learning environment at the task level, the activity level, the course or program level, or even at the institutional level (Bonk& Graham, 2005). Blended learning is defined as a combination of instructional media learning systems, and it typically links face-to-faceinstruction with computer-assisted student learning and management systems (Baugher, Varanelli, & Weisbord, 2003; Bonk & Graham,2005; Brew, 2008; Georgouli, Skalkidis, & Guerreiro, 2008; Yudko, Hirokawa, & Chi, 2008). The blended learning mode offers additionalstudent learning approaches that complement, and change, the students learning and critical thinking processes into various levels ofblended learning engagements (EL-Deghaidy & Nouby, 2008; Sendag & Odabasi, 2009). Such approaches include: (1) on-line competitivesimulations, (2) business negotiations and role plays, (3) interactive and dynamically changing business case and problem solving activi-ties, (4) virtual classrooms suites, (5) video conferencing or teleconferencing (to external locations), (6) social networks (like Facebook), (7)gaming-style interactive networks (like SecondLife), and (8) many direct workplace-linked learning tools. At the higher-end of blendedlearning, some limited flexible choice options (like personal or team-based negotiated additions) may also be included in the educator’slearning mode offerings. These higher-end blended learning approaches move the student’s blended learning solution towards a flexiblelearning approach.

The flexible learning mode moves the student learning experience to an: ‘anytime’, ‘anywhere’, ‘anyhow’ learning environment. Theflexible learning mode encapsulates the ‘what’, the ‘where’, the ‘when’ and the ‘how’ of the learning occurrence (Hill, 2006). It is concernedprimarily with the management and administration of the provision of individual student access, content, delivery style, logistics and pro-ductivity (Silva & McFadden, 2005; Taylor, 1998). Bryant, Campbell, and Kerr (2003) suggest flexible learning focuses on the learner and thelearner’s needs. Collis and Moonen (2002) add class time, course content, instructional approach, learning resources, location, technologyused, entry and completion dates, and communication media as other components of the flexible learning framework. Hill (2006) proposesflexibility is a mix of flexible delivery, combining associations of blended learning and flexible learning with high degrees of pathways flex-ibility and relative pathways strengths. The ‘how’ dimension of flexible learning captures individual student processes, and targets the lear-ner’s quality experiences along with the learner’s personal characteristics, learning style, work responsibilities, learning needs and desiresand personal circumstances (Nikolova & Collins, 1998; Nunan, George, & McCausland, 2000; Smith, 2001). Thus, flexible learning encap-sulates a complex mix of timing flexibility, content flexibility, entry requirements, instructional and resources deployment approaches,and delivery-logistics (Collis & Moonen, 2002). It also delivers aspects of the ‘why’ associated with the learning process. Hence, the curric-ulum is strongly individual and provides individualized student-negotiated, customerized learning services (Hamilton, 2007a).

Thus, the flexible learning mode encapsulates: flexible mode of delivery, flexible access to learning resources, flexible curriculum andassessment, flexible scheduling and flexible study pathways. For example, tertiary students undertaking learning at a location and timeconvenient to them, may or may not, need to attend the tertiary institution’s campus. This gives tertiary students greater freedom to deter-mine the pace and timeframe at which they learn, as opposed to traditional or blended prescribed instructional time requirements. Flexiblelearning also allows the student with appropriate prior learning, to choose the content they want to learn. This can be delivered by indi-vidual-student learning contracts, or by independent study, and without compromising academic standards (Wade, Hodgkinson, Smith, &Arfield, 1994). With this additional student choices approach, the ‘mix-and-match’ of modularized learning units (or cross-program learn-ing units) can contribute to learning flexibility.

Hence, although traditional, blended and flexible learning modes are different, their differences are significantly related to their respec-tive instructional approaches (Duke, 2002; Georgouli et al., 2008; Levy, 2005; Reisetter, Lapointe, & Korcuska, 2007). For example, blendedlearning modes are established through the tertiary educator’s applied delivery processes embedded within the chosen blended instruc-tional mode delivery systems. Likewise, a move from traditional instructional mode to blended instructional mode also sees the studentlearner move to a blended learning mode. These three instructional modes discussed above are summarized in Table 1.

The three main instructional approaches to learning shown in Table 1 may be considered as residing along an increasingly complexteaching and learning continuum (Bonk & Graham, 2005; Georgouli et al., 2008). This continuum makes it difficult to directly compareinstructional modes, and it has ensured that there are many views as to what typically constitutes each mode. Thus, to measure instruc-tional modes a range of variables should be captured. Where a broad range of concepts is to be causally investigated, factor analysis com-bined with structural equation modeling (SEM) approaches have recently been pursued (Cunningham, 2008; Hair, Black, Babin, &Anderson, 2010). SEM allows relationships to be modeled after accounting for measurement error, and offers ‘goodness-of-fit’ measures

Table 1Characteristics of instructional modes.

Traditional Blended Flexible

Fully instructor-controlled andinstructor-centred environment

Partially instructor-controlled and instructor-centredenvironment

Fully student-centred learning with instructor acting aslearning advisor

Instructor-determined learningresources set for student use

Some optional learning resources available for studentuse e.g. WebCT, Blackboard or course CD’s

Student negotiated and agreed options regarding learningresources used

Fixed curriculum content andassessment items

Limited negotiated curriculum content and assessmentitems

Fully negotiation on curriculum content and assessmentitems

Fixed time and place for learning andassessment

Limited time and place flexibility for learning andassessment

Fully-flexible time and place scheduling, with multiplestarting and end points for learning and assessment

Fixed study pathways after recognitionof prior learning established

Limited choice of study pathway combinations afterrecognition of prior learning established

Full choiced of study pathway combinations after recognitionof prior learning established

Fixed entry (or exit) points Limited choice of entry and exit points Student negotiated choice of entry and exit points

J. Hamilton, S. Tee / Computers & Education 54 (2010) 1036–1053 1037


to show whether the sample data actually supports the hypothesized theoretical models (Cunningham, 2008). Our research employs SEMto investigate instructional modes as they are perceived by first year tertiary students.

2.1. Learning model

Although tertiary institutions may deploy unique combinations of learning mode offerings, and may sometimes offer a continuum ofunique learning environments, and learning activities, there is scant detailed quantitative evidence that different instructional modes ap-proaches actually deliver significantly different student-perceived learning outcomes. Our research offers a process by which tertiary insti-tutions can understand how their different instructional mode approaches differ, and how these different instructional mode approachesexert differing influences on student learning outcomes. We build this approach from the conceptual Biggs (2003) 3P model of teaching andlearning as shown in Fig. 1.

Biggs 3P model relates presage or characteristics existing prior to the learning engagement (which we term ‘teaching (or instructional)modes’ and ‘personal skills’), process which captures the student learning experiences, and product which captures student learning out-comes (which we split into student-perceived learning skills and student-perceived learning quality delivered). Biggs suggests that theresultant learning outcomes are complex, and that they operate in interaction with each other. He suggests the general direction of effectsmay be represented by heavy arrows as shown in Fig. 1, and that both student factors and the teaching context jointly drive the systemtowards a common set of learning outcomes. Biggs adds provisos that (1) no two classes, or any two teacher–student engagements arethe same, and so the teacher and the individual student may each acquire different results, and that (2) the institution can also influencethe system. Thus, learning is complex, and it is a system determined by many factors. In addition, a change in one area of the learning sys-tem may affect another area.

Biggs links teaching and learning together and his approach represents a set of inter-connected two-way pathways between contrib-uting constructs. Biggs’s model shows teaching contexts typify characteristics prior to engaging in student learning (such as: teachingmethods; classroom climate; assessment methods; and course content), may each influence prior student factors (such as: student priorknowledge about the topic and interest in the topic; student intellectual ability; and student expectations and learning preferences). Theteaching context and the student factors may combine to affect student-perceived approaches to learning (Biggs, 2003). The processes ofhow students learn, in-turn, influences their perceptions of achievable learning outcomes. In addition, Biggs’s model shows student factorsand teaching contexts also exert direct influences to learning outcomes. This Biggs (2003) 3P model and it predecessors have been exten-sively published, and it continues exert influence (Flood & Wilson, 2008; Nemanich, Banks, & Vera, 2009).

Our research examines the Biggs 3P model from an instructional (teaching) modes perspective. Hence, we have rearranged the Biggs 3Pmodel to highlight the influence of instructional modes on the other dimensions of the original Biggs 3P model, and we show this as ourinstructional modes conceptual model of Fig. 2. This approach allows us to show teaching context as the independent ‘instructional modes’variable, and allows us to investigate the model’s relationships using a structural equation modeling approach.

We split the Biggs teaching context into three different teaching modes, and then test the general effects on the student-perceivedlearning system, as it drives towards delivering learning outcomes. We apply the tertiary institution deliverables as applied to differentlearning modes, and assess these against student perceptions using a SEM approach. We build the relationships between each tertiary insti-tution learning mode, and their respective learning-related skills sets. We extend these skills sets by associating them with student-per-ceived learning experiences, learning quality outcomes and a student’s personally acquired learning skill set. Finally, we engage the threelearning modes, fitting them across the Biggs teaching context (instructional modes) block and capturing both the teaching parameters,

Teaching Context(Traditional, Blended

or Flexible)

ObjectivesAssessmentClimate/EthosTeachingInstitutional Procedures

Student Learning Outcomes Block

Quantitative (Facts and Skills)Qualitative (Structure and Transfer)Affective Involvement

Learning Focused Activities

Appropriate or DeepInappropriate or Surface

Student Factors

Prior Knowledge AbilityMotivation

Presage Process Product

Fig. 1. Biggs 3P model, Biggs (2003, p.19).

1038 J. Hamilton, S. Tee / Computers & Education 54 (2010) 1036–1053


and the current ethos of the tertiary institution. We capture Biggs’s student prior knowledge block as a personal skill set for each studentengaged in the learning mode encounter. We also capture Biggs student learning activities as individual-student learning experiences andperceived learning quality, and we capture the learning outcomes block via an acquired student skills set relevant to each learning mode.Our key literature support, construct, and measurement items, and their relationship constructs (discussed below), are tabulated in col-umns one and two of Table 3. Measurement items, and their appropriate construct, are presented, along with their respective item load-ings, mean, standard deviation and construct Cronbach alpha.

2.2. Construct development

The constructs, and their relationships, to each learning mode, capture the essence of these tertiary student engagement approaches.The traditional, or face-to-face approach, is built from works by Beattie and James (1997), Bliuc et al. (2007), Bonk and Graham (2005),Chickering et al. (1987), Gamliel and Davidovitz (2005), Hughes (2007), Johnson and Johnson (1998, 1999), McCarthy and Anderson(2000), Miller and Groccia (1997), Moore (1989, 1991), Moore and Kearsley (2004), Navarro and Shoemaker (2000), Novak (1998), and The-roux (2004).

The blended approach is built around works by Baugher et al. (2003), Biggs (2003), Bonk and Graham (2005), Brew (2008), Caladine(1999), Dabbagh and Bannan-Ritland (2005), Delialioglu and Yildirim (2007, 2008), Georgouli et al. (2008), Hamilton and Tee (2008), Hill(2006), and Yudko et al. (2008). Overall, the blended approach captures four key areas: space time; fidelity and humanness (as an enablingand ‘more flexible’ approach); an enhancing practices approach; and a transforming learning approach.

The flexible approach captures five dimensions of flexible learning (time; content; entry requirements; instructional approach and re-sources; and delivery and logistics). This approach is built on the works of Bonk and Graham (2005), Collis and Moonen (2002), Georgouliet al. (2008), Hamilton (2007b), Hill (2006), Nikolova and Collins (1998), Nunan et al. (2000), Smith (2001).

Although similar, the traditional, blended and flexible learning modes learning skills set are different, and the final questions used in thisstudy for each are tabulated in column one of Table 3 as the three constructs: T SKILLS, B SKILLS and F SKILLS. These constructs are devel-oped from works by Allen, Bourhis, Burrell, and Mabry (2002), Boyatzis and Kolb (1995), Duke (2002), and Kretovics (1999). Cross-checkingwith several earlier measurement item questions also ensured respondent understanding of the differences between traditional, blendedand flexible learning modes.

The student factors construct (P SKILLS) represents the personal and related motivational skills set that students bring to the learningarena. These house prior, and system engaging learning ability related measures, and are captured by Allen et al. (2002), Biggs (2003), Boy-atzis and Kolb (1995), Caladine (1999), Collis and Moonen (2002), Delialioglu and Yildirim (2007, 2008), Duke (2002), and Kretovics (1999).

The learning experience construct (L EXP) is built on acquired experiences, instruction, personal instruction and group activities. It cap-tures course delivery flexibility, perceived experiences usefulness, perceived ease of use, relevancy, interactions or socializing with peers,interactions with instructors (Arbaugh, 2000; Arbaugh & Duray, 2002; Davis & Wong, 2007; Dill & Soo, 2005; Douglas, McClelland, & Da-vies, 2008; Finch, 2008; Marks, Sibley, & Arbaugh, 2005; Miller & Groccia, 1997; Sun, Tsai, Finger, Chen, & Yeh, 2008; Wade et al., 1994).

The learning quality construct (L QUAL) captures outcomes related content, content and context linkages, knowledge mastery, learningdiscussions and interactive contacts with educators. It builds a range of measure typically capturing dimensions of: global applicability,perceived educator quality, perceived course and content quality, system quality, technology quality, information quality, service quality(Alves & Raposo, 2007; Chiu, Hsu, Sun, Lin, & Sun, 2005; Collis & Moonen, 2001; Holsapple & Lee-Post, 2006; Johnson, Hornik, & Salas, 2008;Lee, 2006; Lowry, Molloy, & McGlennon, 2008; Smith, 2001; Sun et al., 2008; Wade et al., 1994; McFarland & Hamilton, 2006).

Instructional Mode(Traditional, Blended

or Flexible)

ObjectivesAssessmentClimate/EthosTeaching ParametersInstitution

Student LearningOutcomes Block

Quantitative Facts and SkillsQualitative Structure and TransferAffective Involvement

Learning Focused Activities

Appropriate and DeepInappropriate and Shallow

Student Factors

Prior KnowledgeAbilityMotivation

Fig. 2. Instructional modes conceptual model, adapted from Biggs (2003, p.19).



2.3. Instructional modes model hypotheses

Based on the literature supported constructs outlined above, we now complete our proposed instructional modes model with literaturesupporting each hypothesized path relationship between construct pairs. This final instructional modes model is shown as Fig. 3. These con-structs are grouped within four dotted line boxes representing the four instructional mode conceptual model blocks of Fig. 2.

Trigwell and Prosser (1997) suggested under different instructional modes, different learning outcomes may be generated. Cybinski andSelvananthan (2005) showed flexible instructional modes delivered different levels of learning outcomes when compared to traditionalinstructional modes, and Dowling, Godfrey, and Gyles (2003) and Nemanich et al. (2009) showed traditional and on-line (blended) instruc-tional modes approaches again delivered different learning outcomes. Hence, we predict the three instructional modes (traditional,blended and flexible) should provide differing levels of learning outcomes. This is measured as hypothesis H3 (instructional modes affectlearning outcomes), and is further supported by Dart et al. (2000), Nemanich et al. (2009), and Silva and McFadden (2005).

Georgouli et al. (2008) found blended mode offered both higher learning experiences and higher learning outcomes when compared totraditional mode approaches. This is measured as hypotheses H4 (instructional modes affect learning experience), and H9: (learning expe-rience affect learning outcomes). Others (Cybinski & Selvananthan, 2005; Dart et al., 2000; Nemanich et al., 2009; Silva & McFadden, 2005;Zhang, 2000) also support these relationships.

Levy’s (2005) research showed blended instructional mode enhanced learning skills. Thus, hypotheses H1 (instructional modes affectpersonal skills) and H2 (instructional modes affect learning skills) are both established, and again supported by Dart et al. (2000), Nemanichet al. (2009), Reisetter et al. (2007), and Silva and McFadden (2005).

Duke (2002) and Reisetter et al. (2007) investigated learner interactions, expectations and learning skills. They found different learningskills, under different instructional modes, were enhanced in different ways, and thus resulted in different learning experiences and out-comes. These are further measures of hypotheses H1 and H2, and they also support hypotheses H5 (capturing the personal skills affects onlearning experience) H6 (capturing the different personal skills affecting learning skills outcomes); H7 (linking personal skills with learningskills) and H10 (linking learning skills to learning outcomes). Other literature supporting these hypotheses resides in Cybinski and Selva-nanthan (2005), Dart et al. (2000), Silva and McFadden (2005), Zhang (2000). Wang and Braman (2009) investigated blended activities inthe virtual world ‘Second Life’, and concluded learning experiences enhanced learning outcomes, thereby supporting H8.

Hence, based on the materials outlined above we now empirically test (using student-perceived responses) our literature supportedinstructional modes model as displayed as Fig. 3. The 10 literature supported instructional modes model hypotheses are each proposed assignificant (p < 0.05) net pathways between constructs. Two-way feedback interactions between constructs as suggested by Biggs(2003) may occur, but each net instructional effect pathway between constructs is predicted to predominate in the direction shown inFig. 3. The Biggs (2003) strong influence pathways (shown in Fig. 3 as heavy arrows) support this view, and each net pathway is predictedto be significant. Drew and Watkins (1998), Hall, Bolen, and Gupton (1995), and Wong and Watkins (1998) have used similar linear SEMapproaches when studying the Biggs 3P model.

Instructional Mode

Learning Activities

Instructional Modes

Personal Skills

Learning Skills

Learning Outcomes

Learning Experience

Student Factors

Student Learning Outcomes

Block

H9

H8 H10

H6

H4

H3

H2

H1

H5

H7

Fig. 3. Instructional modes model.

Table 2Demographic perceptions of learning modes.

Instructional mode Study mode Year Attendance Gender

Full-time Part-time Total Lectures only All day Female Male205 22 227 29 198 150 77

Traditional 4.84 4.88 4.85 4.81 4.83 4.85 4.84Blended 5.15 5.50 5.18 5.20 5.17 5.20 5.14Flexible 5.87 6.01 5.89 5.83 5.89 5.95 5.76



3. Research study

The instructional modes model of Fig. 3 is investigated using a factor and SEM approach. The instructional model relates student percep-tions of different instructional modes to resultant student-perceived factors, activities and outcomes blocks.

The survey instrument was based on literature perspectives and then supported by a focus group of 15 academics and students, and twopilot studies – one involving 49 post graduates and one with 36 undergraduates. This process delivered several question refinements whilstremoving ambiguities, leading questions and bias. In all questions (except for demographics) Likert scale measures were used, and all weremeasured on one to seven point strongly disagree to strongly agree Likert scales. Reverse coded questions were also incorporated. For con-struct purposes, and ease of interpretation, this study’s Table 3 analysis questions are presented uni-directionally.

This study conducted in March 2009, captured first year tertiary business student during week 5 of their 13 week semester. It captureddata across two tertiary institution campuses. Each campus was based in a different city. Students were sampled across subjects at thesame time. Those with even birthday dates were asked to complete the survey. The survey was distributed to students with an optionto mail-in their completed survey if they felt uncomfortable completing the survey during the lecture. This process removed the possibilityof students completing the survey twice, but did not eliminate the possibility of non-response bias caused by student absence, or by non-completion. A 2% mail return was also received.

From a possible first year business student cohort of six hundred and fifty two hundred and five full-time, and twenty two part-timerandomly selected first year business studies students (totalling 227) valid surveys were obtained. These first year business students stud-ied across a range of disciplines including management, marketing, accounting, finance, economics, tourism, and information systems. Oursample data set was of sufficiently size to ensure accuracy in SEM calculations (Byrne, 2001; Kline, 2005).

Table 2 captures the student’s perceptions of traditional, blended and flexible instructional modes. For internal consistency, we use thecharacteristics of instructional modes listed in Table 1 as guide, and asked students whether they preferred face-to-face teacher managedclassroom learning (traditional); or a mix of part classroom learning and some on-line learning options (blended) or full individually nego-tiated learning situations (flexible). These instructional modes questions were then mapped against the selected demographics as shown.

Twenty nine students attended the tertiary institution for the required three hour lecture only, whilst 198 students attended the tertiaryinstitution for the whole day. One hundred and fifty females and seventy seven males responded. T-tests for each learning mode showedresponse differences between males and female were insignificant (p > 0.05), and the data set was deemed representative of both groups.One hundred and seventy four students had a low earning capacity, with 77% earning below $20,000 per year, and over 99% were of Aus-tralian nationality. One hundred and eighty five students responding (85%) were under twenty five years of age.

Table 2 indicates a consistency of the increasing acceptance of blended and flexible instructional modes over traditional instructionalmodes for first year tertiary institution business students – studying either full-time or part-time, and across different cities. The sametrend exists for students who attend their 3 h lectures (and then depart the campus), and for those attending their tertiary institutionall day. This trend is also consistent across genders. Thus, across key demographic areas, first year business students consistently displaygreater levels of acceptance towards more diverse instructional mode approaches. This may also suggest first year business students per-ceive greater learning quality and personal learning (or value) outcomes are presented when the tertiary instructional approaches migratefrom a traditional to a blended instructional approach, or even through to the more complex flexible instructional delivery option.

4. Structural equation modeling

Factor reduction of relevant survey related measures to single indicator constructs establishes the internal consistency reliabilities ofeach construct. Once reliability is established for each instructional modes block, Munck’s (1979) equations are used to build the necessary‘construct loads’ and ‘construct errors’ measures for single-indicator SEM path analysis. These are shown in Table 3, along with their con-struct means, standard deviations and Cronbach alpha’s. In Table 3 high Likert values indicate strong positive effects, and the means andstandard deviations show the data set is suitable for SEM analysis (Cunningham, 2008; Hair et al., 2010).

4.1. Constructs development

One hundred and six survey questions were used to establish the instructional modes model constructs. Factor reduction typically re-duced eight plus potential factor measurement items to the final three to six construct measurement items used in this study as shownin Table 3. These final construct item measures all present residuals below 0.05 (with no cross-loadings above 0.25), thereby preservingthe construct measurement item, internal consistency and discriminant validity (Cunningham, 2008; Gefen, Straub, & Boudreau, 2000; Hairet al., 2010). Although based on literature perspectives, several other measures are new, but are supported by focus group, and two pilotstudies. Hence, measurement item Cronbach alpha’s even as low as 0.3 are acceptable for this SEM approach (Cunningham, 2008; Hairet al., 2010). The lowest measurement item Cronbach alpha used herein is 0.57 is readily acceptable (Gefen et al., 2000; Hair, Anderson,Tatham, & Black, 1998), and it is associated with a strong construct alpha of 0.75. This 0.57 measure is also supported by literature, andso is retained in the construct. Under future studies, further investigation and refinement of lower-end construct items may be beneficial.These nine constructs displayed in Table 3 also show the construct measurement item alpha’s, the construct means and standard devia-tions, and the construct alphas. From these measures calculated construct loads and construct errors (Munck, 1979) are developed forthe single-indicator SEM path analysis approach.

4.2. Instructional modes model developments

The instructional modes model was tested under using SPSS 16 and Amos 16 (Amos Development Corporation 2008), case items wereexamined within the theoretical context of each scale and 46 potential construct items were removed during factor reduction for substan-tive and statistical reasons (Anderson & Gerbing, 1988; Cunningham, 2008). Twenty one measurement items within the survey deliveredlearning mode and valid response checking tools similar to those of Table 2, and five items allowed other groupings.



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Unidimensionality, reliability and convergent and discriminant validity were evaluated for 35 acceptable construct items representing224 construct case items (after three outliers were removed during purification). Here, no construct items exhibited modification indicesgreater than four, no standard residuals were greater than two, and no standard parameter estimates were less than 0.50. The compositereliability for each construct was acceptable with each being 0.75 or greater (Nunnally, 1978). We also tested for significant two-way feed-feedback pathways between constructs as suggested by Biggs (2003), but as expected in this study, none were significant, and uni-direc-tional student learning pathways resulted. To further investigate this feedback area matched surveys from tertiary institution instructorsand from student would be required (Hamilton, 2007b).

Only three models could be generated. These are displayed as Figs. 4–6. Only one model could be generated for each of instructionalmode. Relevant reliability and validity ‘goodness-of-fit’ measures are tabulated in Table 4. In each case, construct validity was excellentacross all models – with chi squared to degrees of freedom ratios around the value ‘two’, and p values above 0.05. The RMSEA, RMR,CFI, GFI, AGFI, and TLI values all indicate excellent model fit for each instructional mode model (Gefen et al., 2000). The GFI minus AGFIratio remained under 0.06 for each instructional mode, and again supported excellent model fit. The Bollen-Stine p (2000 bootstraps),for each instructional mode remained well above 0.05, further validating the fit of the models (Bentler, 1995; Bergozzi & Yi, 1988; Blunch,2008; Byrne, 2001; Cunningham, 2008; Cunningham, Holmes-Smith, & Coote, 2006; Hair et al., 2010; Joreskog & Yang, 1996; Kline, 2005;Loehin, 1992).

These three pathways models each deliver high quality results, but due to the sample size available, a calibration/validation split-sam-ple, successively-restricted, comparative-fit, invariance approach (Byrne, 2001; Standage, Duda, & Ntoumanis, 2005) was unable to be per-formed due to sample size limitations (Hair et al., 1998, 2010). Hence bootstrapping (2000 bootstraps), supported by near normal MLcharts, was used to indicate the avoidance of possible calculation misspecification errors, and to further validate model fit (Cunningham,2008; Hair et al., 2010).

4.3. Instructional mode model comparisons

As proposed in Fig. 3, All instructional modes model hypotheses except hypothesis ten (H10), are successfully established as significant forthe flexible instructional mode approach, but differences with other models do exist. Comparisons between instructional models are dis-played in Table 5. The blended instructional mode approach displays one less significant path than the flexible one, whist the traditionalapproach displays two less significant paths.

Our SEM shows that significant interaction relationships exist within the learning skills construct. These relationships are not hypoth-esized in our original instructional modes model. Under structural equation modeling, an overall learning skills framework within the student

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Fig. 4. Traditional instructional modes model (standardized estimates).



learning outcomes block exists as three significant learning skills constructs – with the traditional learning skills and the flexible learningskills constructs supported by a blended learning skills construct. This learning skills framework, within the student learning outcomes blockconstruct block, is consistent across all models (and also has input from the other learning blocks within the instructional mode model).Hence, students may perceive their acquired and engaged blended skills as a core attribute within their overall student-perceived learningoutcomes framework. The structure of this learning skills framework suggests the blended skills construct further contributes to enhancinga student’s traditional learning skills, whilst also acting as a scaffolding into components of a higher student learning skill, which herein istypified by the flexible learning skills construct.

Seven of the ten hypotheses offer significant pathways for the traditional instructional modes model. The pathway between the outputconstructs of learning skills and learning quality (H10) is not established. The pathway from teaching context construct learning modes toboth the student factors construct of personal skills (H2) and to the students learning outcomes construct of learning outcomes (H3) is alsonot established, but two pathways from the learning activities construct of learning experience to the students learning outcomes con-struct of learning skills (H8) output block do exist.

Eight of the ten hypotheses offer significant pathways for the blended instructional modes model. The pathway between the output con-structs of learning skills and learning quality (H10) is not established, nor is the pathway from teaching context construct of learning modesto the students learning outcomes construct of learning outcomes (H3), but two pathways from the learning activities construct of learningexperience to the students learning outcomes construct of learning skills (H8) output block do exist.

Nine of the ten hypotheses offer significant pathways for the flexible instructional modes model. The pathway between skills and quality(H10) is not established, but two pathways from the learning activities construct of learning experience to the students learning outcomesconstruct of learning skills (H8) output block do exist.

Our instructional modes model approach, showing key SEM pathways to induce student-perceived learning outcomes, also captures thestrong directional arrows of the Biggs 3P model (as depicted earlier in Fig. 1), as significant paths H4, H5, and H9. This approach also exposesthe additional significant key student-perceived learning pathways of H1, H6, H7, and H8 across all three instructional modes models, anddisplays path and path strength differences between the teaching and leaning modes models as predicted. Here more engagement withthe student – typified by a blended over a traditional learning modes approach, is shown to deliver higher net student-perceived learningeffect outcomes Thus, Biggs pathways to learning outcomes are supported, but we have extend his work with our instructional modesmodel, and with its application to traditional, blended and flexible instructional modes. Our research shows traditional learning modes,are likely to deliver weaker learning outcomes deliverance systems and via fewer student-perceived learning paths, within the same ter-tiary institution and within the same year level and faculty.

Instructional modes model comparisons of standardized beta weight path strengths shows path differences emerge between models.Traditional instructional modes engage weakly with students, and show weak path strengths. This mode contributes weakly to the

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Fig. 6. Flexible instructional modes model (standardized estimates).

Table 4Instructional modes model validation.

Instructional mode df v2 RMR GFI AGFI NFI IFI CFI TLI RMSEA p (Bollen)

Traditional mode 11 15.50 0.05 0.98 0.95 0.97 0.99 0.99 0.98 0.04 0.90Blended mode 10 15.69 0.03 0.98 0.95 0.98 0.99 0.99 0.98 0.05 0.73Flexible mode 9 10.82 0.04 0.99 0.96 0.98 1.00 0.99 0.99 0.03 0.98

Table 5Hypothesized pathways.

Structure path Traditional Blended Flexible

Path estimate p Value Path estimate p Value Path estimate p Value

H1 Instructional mode ? P SKILLS 0.34 –*** 0.52 –*** 0.74 –***

H2 Instructional mode ? B SKILLS 0.41 –*** 0.13 –*

H3 Instructional mode ? L QUAL 0.15 –*

H4 Instructional mode ? L EXP 0.25 –*** 0.28 –*** 0.34 –**

H5 P SKILLS ? L EXP 0.53 –*** 0.45 –*** 0.35 –**

H6 P SKILLS ? B SKILLS 0.48 –*** 0.24 –** 0.37 –**

H7 P SKILLS ? L QUAL 0.38 –*** 0.37 –*** 0.28 –**

H8 L EXP ? T SKILLS 0.86 –*** 0.86 –*** 0.86 –***

L EXP ? F SKILLS 0.54 –*** 0.55 –*** 0.55 –***

H9 L EXP ? L QUAL 0.54 –*** 0.55 –*** 0.50 –***

H10 B SKILLS ? L QUAL Not established Not established Not establishedT SKILLS ? L QUAL Not established Not established Not establishedF SKILLS ? L QUAL Not established Not established Not establishedB SKILLS ? T SKILLS 0.17 –*** 0.15 –** 0.18 –***

B SKILLS ? F SKILLS 0.20 –*** 0.18 –** 0.20 –**

* Significant at p < 0.05.** Significant at p < 0.01.

*** Significant at p < 0.001.



student’s personal skills set, and weakly to their learning experience. These, in-turn weakly affect the outcomes sets of traditional skillsgrowth, flexible skills growth and learning quality. Blended instructional modes engage an additional direct pathway directly influencingthe student’s blended skills acquisition, and display stronger path strengths when directly compared to like paths within the traditionallearning mode model. Flexible instructional engages more fields and also delivers even stronger pathways to the learning experience (LEXP) construct and to personal skills (P SKILLS) construct.

Differences between blended and traditional instructional modes is further highlighted using a ‘Total Effects of Instructional Modes’ ap-proach, with the total effects value for each construct recorded as shown in Fig. 7. Comparison shows that from every perspective a blendedinstructional approach, when compared to a traditional instructional approach, delivers a major lift in total effects across every learningconstruct. This demonstrates that first year tertiary institution students perceive their learning experiences are significantly expandedwhen blended instructional approaches are utilized. For example, when considering the personal skills construct a one unit change in ‘tra-ditional instructional mode’ delivers a standardized total effects unit change of 34% in personal skill, whilst under a ‘blended learning’mode a one unit change delivers 52% of a unit change in ‘personal skills’. Thus, student personal skills benefit, and grow, much more undera blended instructional mode. A similar situation applies when ‘traditional teaching and learning’ is compared to ‘flexible teaching andlearning’. Here, a one unit (or 100%) change in the flexible instructional construct delivers a resultant 74% of a unit change in personal skills.

The ‘flexible teaching and learning’ mode engages one additional direct pathway to that of blended teaching and learning. This fourth‘flexible teaching and learning’ mode pathway directly influences the key output construct learning quality (L QUAL) construct. In additionto the large total effects measure of the flexible learning personal skills construct, the strong path strength (standardized beta weight of0.74) between the flexible learning mode and personal learning skills path indicates a strong relationship with the flexible instructionalconstruct. Here, the student’s personal skills set is strongly engaged in negotiating, generating, and delivering a suitable personal courselearning options and activities set, and so explains the strength of this flexible instructional to personal skills pathway. The total effectsof the flexible learning personal skills set effects, and lowers, the pathways contributing to the blended skills total effects. However, thesum of the total effects of the flexible instructional personal skills and blended skills constructs approximates those encountered in theblended instructional mode situation, suggesting that blended skills and personal skills may be considered by first year students as some-what similar in nature, and possibly overlapping, or even fitting along a continuum.

First year student trend comparisons of overall total effects for flexible and blended instructional modes shows that overall net totaleffects for each learning mode follow similar patterns, but that flexible learning offers marginal improvements across all bar one field.Blended instructional modes, when compared, across the learning blocks captured herein, offer average student-perceived total learningeffects around 65% greater than those from traditional instructional modes, and flexible teaching and learning modes offer around 80%improvement. Hence, this study models, and shows, that first year students, who are capable of engaging in blended or flexible instruc-tional mode interactions, perceive that either mode delivers them significantly enhanced instructional outcomes to those offered by tra-ditional instructional mode approaches. This conclusion is also supported by our questionnaire cross-checking of selected measurementitems, and by the observations and findings of Table 2. It also appears that first year tertiary students do perceive there are some additionaladvantages to flexible instruction – like enhanced learning quality outcomes.

5. Discussion

This empirical work shows that student-perceived different instructional modes relate the teaching context to the constructs associatedwith perceived student learning activities, factors, and outcomes. Black (1996) suggests learning materials impact on how students learn,and possibly on how they feel about their tertiary course offerings. She shows that students can recognize learning approaches, and under-stand which learning mode approach offers them most effective learning outcomes. Black (1996) also shows that students do recognizedifferences in instructional modes and that these differences do significantly impact on their learning process. In a similar manner, ourstudy uses student perceptions as the data collection approach.

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Fig. 7. Instructional modes constructs set: first year student-perceived total effects.



When our student perception constructs are investigated under a single-indicator SEM approach, the significant, relative learning pathsbetween constructs are exposed. The traditional instructional mode structural equation model of Fig. 4 displays two significant (and lowpath strength) pathways by which to deliver significant student learning outcomes. However, on average, students perceive low net learn-ing outcomes from this mode of delivery. The blended instructional mode of Fig. 5 engages three learning pathways, and each is of greaterstrength to those of the traditional learning mode approach. This indicates stronger student-perceived learning outcomes are deliverableunder blended learning approaches. The flexible instructional mode of Fig. 6 engages four learning pathways to student-perceived out-comes – one with direct linkage to the learning output construct ‘learning quality’. These four flexible learning mode pathways deliverstronger student-perceived learning outcomes to those of blended learning.

Thus, for first year tertiary students, a change in instructional mode approaches from a traditional to a blended (or even to a flexible)approach is desirable if higher student learning outcomes are to be sought. Where such a change is suitably targeted, and then suitablyimplemented, and also well delivered, it is likely that students will experience enhanced learning outcomes. This, in-turn, will likely en-hance student-perceived satisfaction with the educator, with the tertiary institution, and with tertiary learning overall (Marks et al., 2005;Sun et al., 2008). Hence, tertiary institutions should move their instructional modes into the blended instructional arena – possibly embedaspects of flexible learning – particularly flexible content or flexible time. Alternatively, they should move further along the continuum oftraditional-to-blended-to-flexible instructional mode and should experiment within such traditional/blended/flexible modes as to whatactivity mixes best suit the contents of each first year tertiary course they are offering.

Our study suggests that although differences in learning are related to different instructional modes, student learning mode optionsform a learning continuum. We represent this as a ‘Cone of Learning’ continuum – which is displayed as Fig. 8. The Cone of Learning rep-resents increasing complexity in student learning mode offerings and student options arising, as one moves from the base level (or tradi-tional learning mode) through to the more complex flexible learning mode approaches. The three axes for the Cone of Learning encapsulateour study and the three Biggs (2003) 3P student learning construct blocks of Fig. 1 (learning activities, learning outcomes and learning fac-tors). Relative positioning along each of these three axes represents a net measure of the resultant learning construct block and its inherentstudent learning contributions.

Within the Cone of Learning a positional space for a specific set of learning activities, learning outcomes and learning factors may also bedetermined. For example, when more learning activities (like: greater student participation in the levels of learning experiences required)are engaged in conjunction with the inherent student factors brought to the learning situation by the student (like: the student’s personallearning skills, and student-embedded learning motivation), and these support an optimal student learning outcome set (like: acquired,and of value, student learning skills, along with the optimal qualities of the acquired student learning) for the student cohort under a spe-cific instructional mode context, this specific positional space within the Cone of Learning continuum may be located. Hence, a tertiary insti-tution may use this approach, compare its active (or chosen) Cone of Learning positional spaces, and then develop aspects of its desiredstrategic instructional positioning.

We represent the Cone of Learning with no clear boundaries. We believe overlap clearly exists between the different learning modes.Michinov and Michinov (2008) in seeking a suitable blended learning integration mix between face-to-face and on-line learning compo-nents, also support this blurred, learning modes, boundary position. We also suggest the three dimensional axes to the Cone of Learninglikely interact as three right angled dimensions. We note additions to our construct measures may offer additional preciseness to tertiaryinstitution positional space location within the Cone of Learning. For example, expanded subsets of the student learning experience con-struct could house subsets like the complexity, diversity, relevance and depth of learning; or the student factor construct could house sub-sets like the student’s motivational, personal and prior knowledge skills set; or flexible learning subsets could include: content, timing,entry requirements, instructional/resources deployment, and delivery-logistics. Further discussion is captured in the following ‘implica-tions for research’, ‘future research’ and ‘conclusion’ sections.

6. Implications of research

6.1. Theoretical implications

This empirical research supports our instructional modes model and also delivers a form of validation to the Biggs 3P model. It shows thatfor first year tertiary students, statistically significant net-one-directional learning paths do exist, and that all fields of the Biggs 3P modelare significantly inter-related. This research also confirms that the student-perceived primary drivers for effective first year student learn-ing reside within the teaching modes arena. Within this teaching modes arena blended and flexible delivery modes yield higher studentlearning effects than those provided by traditional learning.

Traditional Learning

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Fig. 8. The cone of learning.



This research may be repeated across different year levels with student preferences towards additional blended and flexible instruc-tional modes preferences being predicted. Using SEM and Mplus approaches in conjunction with specially linked surveys, and with addi-tional outcome variables like value, satisfaction, graduate attributes, workplace desired graduate skills, and business outcomes (Hamilton &Tee, 2009) further tertiary institution and workplace connections and new research areas relevant to the tertiary instructional outcomesmay be unlocked. Here, the Mplus-SEM approach may be tapped to unleash strong, multi-level, hierarchical model development options,and to possibly capture dichotomous (or ordered categorical) outcomes constructs within the resultant model.

The teaching modes – which encompass instructional and learning activities and procedures, typically drive significant learning influ-ences onto the student (Black, 1996). These pathways culminate in student-perceived learning outcomes and skills enhancements. Re-search into what constitutes teaching modes, and what are the advantages and disadvantages, costs and benefits of each, broadens thescope of this study, and warrants additional study.

Recent research has show first year students experiences are key success drivers, and these are significant determinants to a student’sdegree completion (Tumen, Shulruf, & Hattie, 2008). Therefore, strategies that help to retain first year students are beneficial to the tertiaryinstitution. This research has the potential to improve the retention rates of first year tertiary institutions, and deliver additional revenuestreams at higher levels of tertiary learning. Here, a move towards the blended and focused learning offerings is projected to raise studentperceptions as to the value of their learning experiences. Thus, a more costly solution may be required at first year, but overall additionalstudents will likely be retained and a revenue neutral or revenue positive solution is possible over the student’s tertiary education life-span. Considerations towards further changes to instructional modes that are engaged beyond first year level present another interestingarea of research.

With appropriate blended instructional course designs – focused at first year student preferences, it is possible to improve the prepared-ness of both the tertiary institution and the students so that access to learning and support is targeted, and thus the risk of failure may bereduced along with improvements in retention rates (Jamelske, 2009; Scouller, Bonanno, Smith, & Krass, 2009). Closely targeted coursedesigns fitting preferred student instructional modes for the tertiary institution’s cohort, may also be matched at suitably meet studentrequirements. This offers yet another important area for research. In this area, and across the instructional modes approaches, there is aneed to investigate the net benefit of work-integrated-learning as an alternative to, or complement to, blended or flexible instructionalactivities.

6.2. Practical implications

Tertiary institutions may also use this information from this study, and their own studies, to better design their courses and task offer-ings. The survey approach can be used to indicate which aspects of blended learning students believe are of most value to them in theirstudies and these areas can then be built into course task offerings. Such approaches can result in an engaging mix of learning activitiesbeing developed within a specific tertiary institution, and these engaging approaches can also drive additional knowledge transfer.

Tertiary institutions can use this research to help build institutional policies related to student learning and their how quality learningoutcomes may be delivered whilst also encompassing full learning delivery systems, training, design costs, lecturer appointments, facilities,and the like. Additional studies around this area, which will be tackled by the researchers in coming studies, may capture and relate theinstructional modes along with an expanded set of learning outcomes, and extend these through to desired graduate attributes and also tofinal employability (Hamilton & Tee, 2008, 2009).

Based on the outcomes of this learning modes study approach it is possible to more skilfully timetable courses – particularly consideringstudent-perceived outcomes, and how they may be best delivered. Here considerations as to how to spread classes, delivery modes, andwhere and when to teach the course (weekend, intensive or full semester) should be included in such studies.

When comparing the traditional learning mode to the blended learning mode, the mix-and-match sets (or customisation sets) of learn-ing tools engaged may require modification. In the blended learning case (with higher net learning outcomes projected) to allow for de-grees of student choice, tertiary institution policies should be constructed to encourage each educator to expand each course’s learningdelivery offerings. Policy could then advise educators on how, and why, they should build (1) engaging multi-mode course options, and(2) virtual and tangible course options, into their curricula designs. The addition of such learning engagement processes is also likely toenhance the student’s perception of the overall learning solution-set being offered. In addition, policies should also recognize that blendedapproaches may require different ingredients, mixes and costs as they are applied to different year levels, and also to different studentcohorts.

7. Future research

7.1. Measurement aspects

This study opens the door to a range of approaches that extend and enhance many previous studies. A basic set of literature-basedempirical measures are established herein, and these may be developed and extended to enhance and expand this field of study. TheSEM approach greatly extends the relationships between variables, and establishes paths which may be utilized by tertiary institutionsas a basis when setting new and broader strategic instructional measures and directions. This moves the research well beyond those ofearly researchers like Black (1996), Wang (2003), and others.

Learning occurs differently in different learning environments, and learning outcomes at different year levels also vary. As learningmodes (and their engaged learning offerings) change, so will the inter-connected learning processes. This vast area offers exciting scopefor many future tertiary measurement studies, and for learning applications studies as applied to additional new and focused e-learningmeasures.



7.2. Theoretical aspects

At the educator level researchers may consider how to operationalize best recipes for each learning mode, and may then consider howto package, customize or even customerize (Hamilton, 2007a) and deliver the solutions as developed.

Another exciting on-line learning area to consider as an adjunct to the instructional modes approach is whether on-line learning is aperceived outcome (Arbaugh & Rau, 2007) or whether it just represents the student knowledge capture, and whether this dilemma differsacross learning modes. This may extend through to the educator’s and to the student’s personal confidence, and even to their respectivepersonal computer skills, and these in-turn may influence perceived values attached to the engaged on-line learning systems. Such poten-tial blockages may also influence the student’s overall quality of learning and learning output.

From the educator’s perspective, the blended (or flexible) learning system and its associated on-line instructional modes should capturemeaningful and appropriate educator-to-student-to-educator interactions that may appropriately influence blended learning outcomesacross different interaction environments. This also extends into considerations as to whether on-line learning systems engage learningsituations that actually deliver success, and also drive, a strong student desire for continuance of this learning mode environment approach,or does the student become bored with this virtual approach, and want a balance of face-to-face and blended or flexible teaching and learn-ing? Alternatively, can instructors and students suitably coalesce in an on-line environment and create strong, competitive, focused on-linelearning communities?

This study indicates a student association of blended skills (as a core attribute within their overall student-perceived learning outcomesframework) likely contributes towards enhancing traditional learning skills, whilst also acting as scaffolding into various learning skillscomponents of flexible learning. Hence, researchers may wish to consider whether students at different year levels of tertiary institutionprogression, already perceive blended learning as a core component to their overall acquisition of learning skills, and also as a key contrib-utor to their net student learning outcomes.

7.3. Management aspects

A Tertiary institution may refine our literature supported instructional and student learning measures, and similar to this study, maythen establish its preferred Cone of Learning continuum positional spaces. This tertiary institution can then build its targeted instructionaldelivery mode systems around its preferred positional spaces, and it can also benchmark and/or compare its competitive learning ap-proaches with others. This area is open for research.

The theory of blended and flexible learning compared to traditional learning is not well explored or understood. For example, outsidecase study approaches, the role and effectiveness of wikis, blogs, podcasts, instant messaging, Web 2.0 on learning delivery is not wellresearched.

Resource constraints, and the tertiary institution’s needs to build, and manage, its multiple learning contexts as blended and/or flexibledelivery systems, requires additional investigation. For example, cost benefit analysis and possibly balanced scorecard studies should bedeveloped.

Incorporation of mobile technologies into tertiary philosophical and pedagogical domains may also open new approaches to instruc-tional and educational research.

8. Conclusion

This SEM study, models and shows, that first year tertiary students, capable of engaging in blended or flexible instructional mode inter-actions, do perceive that either of these modes delivers to them significantly enhanced learning outcomes over those offered by traditionalinstructional mode approaches. Across different age groups and across different income levels, full-time or part-time, male or female firstyear tertiary students, each recognize differences in instructional modes, and both genders also indicate a solid degree of understanding asto what each instructional mode entails.

Three different instructional mode models (traditional, blended and flexible) each display student-perceived differences in the number,and overall strengths of the teaching-to-learning pathways. Traditional teaching offers two significant pathways to the learning sphere.Blended teaching offers three significant teaching-to-learning pathways. Flexible teaching offers four teaching-to-learning pathways.When considered in conjunction with the standardized path strengths, the highest student-perceived total learning effects are experiencedunder flexible learning and teaching modes, and the lowest student-perceived total learning effects are perceived under traditional instruc-tional modes. Hence, tertiary institutions should utilize this approach as an aid to target and then deliver their best tertiary-institution-spe-cific instructional modes. These engaging solutions should be generated for each student cohort and year level, and at the school or facultylevel. With regular monitoring of their situation, tertiary institutions can also determine when, and by how much, they should re-fit theirinstructional delivery. With smart adjustments to their instructional solution tertiary institutions may strengthen their teaching-to-learningpathways, and so target generating greater (and better aligned) student-perceived learning outcomes.

Across all three instructional modes, the instructional modes model, hypothesized in Fig. 3, and tested as Figs. 3–5, deliver a consistent,statistically significant, three construct learning skills subset within the student learning outcomes construct block. Across all three modes,the three construct learning skills subset of the student learning outcomes construct block consists of the traditional learning skills con-struct and the flexible learning skills construct, which are each supported by the blended learning skills construct. In addition, two con-sistent and significant pathways from the learning activities construct learning experience operate into this learning skills subset. Thesetwo pathway linkages, and the different pathway linkages and strengths into the blended skills construct, together suggest a degree ofcomplexity, or cross-over, or linkage, may exist between different learning skills sets, and so adds further support to the likely existenceof a learning continuum – such as the Cone of Learning continuum proposed herein.

One hypothesized construct pathway within the learning output construct (between the skills and quality blocks) termed (H10) is notestablished for any model, further suggesting that the output driver students learning outcomes construct consists of two different



constructs (learning skills and learning outcomes), and both of these constructs may contribute to the total student learning outcomes axis ofthe Cone of Learning.

Based on the measures of our study, and the ideas expressed herein, we propose that ‘at higher levels of tertiary institution instructionalmode engagement, greater learning experiences and additional student factors are employed, and that these, in-turn, result in higher stu-dent-perceived net learning outcomes which may be suitably represented by higher net positioning spaces within the Cone of Learning con-tinuum.’ We further suggest that higher level instructional solutions are achievable by engaging broader student learning outcomes subsets –ones typically capturing broader institutional learning skills sets in conjunction with additional student-perceived learning outcomes includ-ing: satisfaction, value, services, quality and communications measures (Finch, 2008; Hamilton, 2007b).

Tertiary institutions, seeking to drive their particular instructional model preference, can choose to strategically: model, position (withintheir Cone of Learning), benchmark, monitor, and improve their instructional mode student-perceived learning achievements. They canjointly deploy greater student learning experiences, incorporate additional student factors pertinent to their targeting, and focus on estab-lishing their ‘optimal’ student learning outcomes set. Tertiary institutions should also remain agile, and should seek additional refinementsthat further enhance: their student learning experience subsets, their student factor subsets, and their student learning outcomes subsetsinto net highly-targeted instructional solutions – specifically designed for each of their delineated student cohorts.

Instructional researchers are encouraged to utilize SEM with Mplus style research approaches – as these facilitate greater knowledgeand understanding of student-perceived learning deliverables and of student-perceived learning outcomes. Specific linkages to specifi-cally-selected additional learning experience devices – like mobile or dynamic learning tools, may then be developed, tested, and if deemedappropriate, then selectively added to the tertiary institution’s competitive, value adding, overall instructional toolkit.

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