Teachers Choosing Rich Tasks

Abstract

This article reports on the pioneering use in education of Discrete Choice Experiments (DCEs) to assess teachers’ decisions regarding deployment of rich tasks. The incorporation of this quantitative method into what is usually considered the domain of qualitative researchers is an innovative feature of this study. The DCEs enabled rigorous, reliable, and efficient investigation of the relationships between attributes of the complex environment in which teachers operate. The findings articulate the choices made by teachers related to rich task pedagogy, technology use, and other resources. Understanding the influences on these choices will inform the adoption and adaptation of productive technologies, improve dissemination of good practices, and enhance the design of digital technologies, resulting in better student learning outcomes.

Keywords

decision making elementary schools instructional technologies learning processes/strategies statistics survey research

This article reports an investigation of teachers’ preferences for rich task pedagogies and considers if learning technologies have a moderating impact on these preferences. Rich tasks have a range of characteristics, such as purposeful connections to the world beyond the classroom, and offer varied opportunities to meet the different needs of learners. A body of literature indicates that rich tasks are valuable components of teachers’ pedagogical repertoires (e.g., Education Queensland, 2002; National Council of Mathematics, 2000; Newman, Marks, & Gamoran, 1996; Snape & Fox-Turnbull, 2013). Consequently, policies and curriculum have encouraged teaching practices that involve rich task elements (Education Queensland, 2002; National Council of Mathematics [NCTM], 2000).

Another key policy issue in Australia (Department of Education, Employment and Workplace Relations [DEEWR], 2011, 2013) and internationally (Organisation for Economic Co-operation and Development [OECD], 2013, 2010) concerns school, teacher, and student engagement with technology for learning. Research on teacher engagement with learning technologies is extensive (e.g., see Ertmer, Ottenbreit-Leftwich, Sadik, Sendurur, & Sendurur, 2012; Mueller, Wood, Willoughby, Ross, & Specht, 2008). Recently, it has been suggested that various learning technologies, including interactive whiteboards (IWBs), may facilitate or disrupt the adoption of rich task pedagogies by teachers (Hedberg, 2006; Kennewell, Tanner, Jones, & Beauchamp, 2008; Schuck & Kearney, 2008). Research on how such engagement with learning technologies moderates teachers’ perceptions about use of rich tasks is likely to be useful, given the proliferation of learning technologies in the classroom. This would inform policies on curriculum development and decisions about resource allocation.

Teachers make choices daily about their teaching approaches, tasks, and the technologies they will use. These decisions are located at the interface between technology, pedagogy, and discipline expertise (Koehler & Mishra, 2008). It has previously been difficult for researchers to assess the relative importance of the many factors that influence these choices about the pedagogies, technologies, and activities that teachers use. This study uses a unique quantitative method, Discrete Choice Experiments (DCEs), to interrogate these complex decision processes. The incorporation of this quantitative method into what is usually considered the domain of qualitative researchers is an innovative feature of this study. DCEs provide insights into teachers’ overall preferences for rich task pedagogy and the manner in which rich tasks are moderated by the availability of a learning technology, in this study, the IWB. The selection of IWBs as the moderating technology was due to their multifaceted technological nature, the diversity of possible associated teaching approaches, their prevalence in schools, and their familiarity to large numbers of teachers (Kennewell et al., 2008).

Background

Given that the study discussed in this article concerns teacher preferences for rich tasks moderated by a learning technology, relevant research on rich tasks and on the use of learning technologies, in particular the use of IWBs, was used to inform the study.

Rich Task Pedagogy

Rich tasks have long been considered a valuable component of teachers’ pedagogical repertoires (Newman et al., 1996). Rich tasks is a generic term that describes activities characterized by: authenticity in their relationship to real-world application and context, pluradisciplinarity, resource intensity, being governed by student direction in assessment and task exploration, encouragement of reflective practices among students, and involvement of students in collaborative approaches to learning (Education Queensland, 2002; Henningsen & Stein, 1997; Shimizu, Kaur, Huang, & Clarke, 2010). The intention of a rich task is both to highlight the quality of the learning outcome and the intellectual strategies students acquire in the process of completing the task, and to guide curriculum planning across a significant span of schooling. Employing rich tasks may require some teachers to adopt new approaches to teaching, learning, and assessment. We note that rich tasks are not unique in their attributes. For example, a similar concept to rich tasks is that of Model Eliciting Activities, or MEAs (Lesh & Harel, 2003). Common characteristics of MEAs and rich tasks lie in their alignment with real-world situations, the social value of the activity, and the complexity and meaning-making required by the task. There is extensive literature about the use of MEAs in mathematics.

In Australia, the emphasis on rich task pedagogy builds on work in the United States on authentic pedagogy, its adoption by teachers, and its impact on student performance (Newmann, 1993; Newmann et al., 1996; New South Wales Department of Education and Training [NSW DET], 2003a). In an authentic pedagogy, students develop deep understanding and apply their learning to significant realistic problems. It is noteworthy that although some adoption has been motivated by curriculum policies and education department–endorsed pedagogical frameworks (e.g., Education Queensland, 2002; NSW DET, 2003b), teachers are often selective in which rich task elements to adopt in their own practice. Underlying such preferences, teachers may have established their own beliefs about those elements of rich task pedagogies that are likely to create greater levels of student learning outcomes relative to others (Handal & Herrington, 2003). Also, teachers must balance their choices against competing considerations in terms of other factors, such as how each rich task element may aid or compromise perceptions of student enjoyment or require greater levels of lesson preparation.

Much of the literature on rich tasks has been in the area of mathematics education. Studies have long discussed the merits of rich task learning in terms of the mathematical outcomes for students in diverse contexts (Griffiths, 2010; Henningsen & Stein, 1997; Shimizu et al., 2010). Downton, Knight, Clarke, and Lewis (2006, p. 9) provide a checklist of rich task attributes for junior primary to junior secondary school mathematics, including the following: assessment that connects naturally with what has been taught, provision of a measure of choice or “openness,” and tasks that can be successfully undertaken using a range of methods or approaches. The task might address a range of outcomes, authentically represent the ways in which knowledge and skills will be used in the future, and elicit a range of student responses. Given that the current study needed a content area in which to investigate rich tasks, the interest in such tasks indicated in the mathematics education literature suggested that mathematics was an appropriate example of a subject area in which to locate the study.

One of the factors that may influence teachers’ employment of rich tasks is the characteristics of the learning technologies available in the classroom (Hedberg, 2006; Kennewell et al., 2008; Schuck & Kearney, 2008). Hence in the next section, we consider research on the adoption and use of learning technologies, and then discuss the IWB as the learning technology investigated in this study.

Teacher Use of Learning Technologies

Teachers make daily choices about what technologies they employ in teaching and about how these technologies can be used to support learning by their students. These choices are intricately guided by teachers’ belief systems, including their self-efficacy and attitudes toward technology (Ertmer et al., 2012; Mueller et al., 2008) and their competence levels and access to technology (Voogt & Knezek, 2008). However, Somekh (2008) also argues that teachers are not necessarily to blame when schools do not take up information and communication technology in innovative ways. Whatever their individual attitudes, beliefs, and competency levels, teachers are bound by regulatory frameworks and sociocultural restrictions and school leaders need to minimize such obstacles to encourage teachers’ use of new digital pedagogies.

The recognition that various barriers and facilitators to teacher technology integration exist is consistent with theories of technology acceptance and adoption discussed in other research domains. For example, in Davis’s Technology Acceptance Model (see Davis, 1989), the roles of the perceived usability and perceived usefulness of a technology are shown to be important in driving behavior. Venkatesh and Bala (2008) further extend the Technology Acceptance Model to consider how experience moderates various relationships, including the impact of usability on behavioral intentions.

Similarly in the context of teaching, Ertmer et al. (2012) suggest that technology integration practice is impacted by first- and second-order barriers. First-order barriers comprise external factors to the teacher (e.g., resources, training, and support), whereas second-order barriers refer to internal factors such as confidence, beliefs about impact of technology on student learning, and the value of the technology itself.

The primary aim of the current research is to examine teachers’ perceptions of how the role of rich tasks will affect a number of outcomes including student learning, student enjoyment, preparation, and overall lesson choice. The study considers whether technology moderates these perceptions and choices. To do so, however, we also need to account for how the perceived value of the technology directly impacts on lesson choices and how this valuation varies for different teachers. We elaborate on this below.

First, we consider how technology impacts teacher beliefs about the ease of preparation of lessons, an outcome analogous to the concept of ease-of-use studied within the technology acceptance model (e.g., Davis, 1989). Davis (1989) defines perceived ease-of-use in terms of whether using a particular system would minimize effort. Similarly, Burke (2013) demonstrates that the usability of a product is valuable in making choices, but highlights that decision makers will often trade off usability against other competing attributes of a product. In the same fashion, we ask teachers to consider how technology impacts the ease of preparing a lesson alongside competing factors that may also impact on preparation time, such as whether the lesson is resource intensive or involves teachers preparing a test for students to complete.

Second, we account for how preference for technology varies across teachers in terms of their ability to readily access the technology in the classroom and the level by which they perceive themselves to be experienced with the technology. Such individual differences in teachers’ accessibility and perceived competence levels with technology have been highlighted by Voogt and Knezek (2008) and Ertmer et al. (2012).We examine whether teachers’ access and familiarity with the technology impact their evaluation of the technology in terms of their perceptions that the technology platform is more attractive in terms of maximizing student enjoyment and student learning.

This study uses IWBs as the technology context for considering teachers’ decision making about rich tasks. The reported benefits of IWB usage include significant teacher satisfaction, flexibility, efficiency, motivation, support of preparation, interactivity, ease-of-use, and suitability for whole class teaching (British Educational Communications and Technology Agency [BECTA], 2008; Kennewell et al., 2008; Smith, Higgins, Wall, & Miller, 2005). Kennewell and Beauchamp (2007) report that such technologies are able to promote engagement among those less able, maintain attention and focus, and encourage reflection, collaboration, retention, and transfer. However, similarly to Hall and Higgins (2005), Schuck and Kearney (2008) suggest that IWBs are mainly being used to reinforce traditional teaching approaches. Kennewell et al. (2008) noted in their U.K. study that IWBs in schools have been met with high rates of adoption and interest. In Australia, IWBs are prevalent in schools and consequently teachers are likely to have formed a view about their use. Thus, there is a prima facie case for considering IWBs as an example of a moderating technology in that they have influenced, reinforced, or altered educational practices in schools.

To understand the array of factors that influence teacher decisions about rich tasks and their moderation by IWBs, we employed a DCE methodology. This methodology is new to education and allows the examination of the complex, dynamic relationship between educational technologies and the pedagogical choices that underpin effective teaching and learning in the classroom.

A New Education Research Tool: DCEs

This research uses a set of DCEs and an associated quantitative set of discrete choice models. This method systematically varies rich task elements and asks teachers to indicate which combination of rich task characteristics and learning technology contexts (in this case, using IWB or not using IWB) would most likely be selected by them. The DCEs also provide explanations for teachers’ choices in terms of which tasks and contexts would enhance student learning, make lesson preparation easier, and enhance student enjoyment. The method is an innovative approach to the measurement of teachers’ preferences for particular attributes of rich tasks, and it ascertains if and how these are moderated by IWBs.

A DCE is able to produce data that enable the estimation of a predictive model. The model reveals the systematic relationship between variation in the characteristics of a carefully designed set of competing offerings and a resulting discrete choice outcome (Louviere & Woodworth, 1983). This holistic assessment is viewed as one in which a decision maker weighs each characteristic depending on its importance. Thurstone (1927) originally developed the method for paired comparisons based on Random Utility Theory (RUT), with extensions to the method made by McFadden (1974). RUT-based models propose that the latent utility value and preferences for competing offerings can be decomposed into two additive components, a systematic (explainable) component and a random (unexplainable) component (Ben-Akiva & Lerman, 1985). The systematic component is able to be modeled as a function of observable and measurable attributes; in the present context, these being the rich task characteristics describing a lesson and the technology used to deliver it. In essence, changes in factors that matter more for teachers will be associated with greater changes in choice. Although new to education, the method has been used in a vast range of settings (Louviere, Hensher, & Swait, 2000).

DCEs provide several useful characteristics for investigating teachers’ preferences for rich task options in their choices of lesson delivery. First, the use of experiments provides a level of systematic control over the offerings that teachers will evaluate, offering advantages over other methods, particularly those relying on preferences being revealed from correlated instances. For example, the variation in one rich task characteristic can be controlled so that it is uncorrelated with the variation in another characteristic.

Second, choice model researchers often make the distinction between revealed and stated preference data. Revealed data are based on what is observable in the marketplace and often characterized by high levels of correlation-making models susceptible to biases from multicollinearity (Louviere et al., 2000). Stated preference data, however, are based on how humans make choices in instances that may not be observable in the real world. Empirically, decision rules are strongly correlated across both settings (Ben-Akiva & Morikawa, 1990). In the present context, this offers additional advantages in how teachers were able to respond to rich tasks options that they had not yet experienced in their own classrooms due to various factors (e.g., curriculum requirements, preferences of co-teachers, lack of resources).

Third, if teachers have a strong history of conducting lessons in a certain way, it is difficult to understand their preferences for alternative teaching strategies merely through observation. A DCE has the advantage of being able to offer a wide range of optional scenarios, which then allows individual teachers’ preferences to be revealed and modeled more comprehensively than if they were based on only what the teachers currently do.

To entice respondents to reveal realistic behavior, DCEs simultaneously present various factors that must be traded off rather than being chosen in isolation (Louviere & Woodworth, 1983). This reduces the bias that arises from participants’ tendencies to respond in a socially desirable way (Auger, Devinney, Louviere, & Burke, 2008; Carson & Groves, 2007; Kocakaya, 2011). In this regard, DCEs are preferable to other methods that ask respondents to consider factors in isolation one at a time, such as when items are rated on Likert-type scales (Louviere & Islam, 2008). For example, a teacher may have rated both the value of collaborative learning environments and authenticity as being desirable in pedagogy, but the subsequent DCE used in this study forced teachers to consider these and other factors in terms of which was more preferable and which would maximize desirable outcomes such as student learning. DCEs are also not subject to other criticisms that multiitem rating scale approaches often receive in relation to various types of biases due to differences in response styles and cognitive effort (Louviere & Islam, 2008). For these reasons, DCE presents a worthwhile and novel approach to investigating teacher preferences for rich task pedagogy and how these may be moderated by IWBs.

The Study

This article reports a study that took place from 2010 to 2012. It used DCEs as a quantitative research method to investigate the following research question: How do teachers’ pedagogical choices, moderated by the use of IWBs, influence their provision of rich learning experiences?

Participants

The DCE was completed by 268 primary school teachers from all Australian states and from both the government and nongovernment sectors (68% in government schools). The majority of respondents were female (88%). Respondents taught at schools that ranged in size from less than 25 students to more than 1,000 students, with an average of 432 students. The ages of respondents varied from early 20s to early 70s, with an average of 39 years. Most were full-time teachers (55%), the others being part-time (43%) or teachers who had left the profession within the last year (2%). A large proportion of teachers had access to an IWB (83%), whereas 63% described their experience with IWBs as intermediate or advanced. The students taught by respondents ranged in age from 4 to 12 years.

Survey Instrument

Rich task scenarios were developed for the DCE used in this study. These scenarios were informed by literature on rich task characteristics, a previous survey, and a series of focus groups with practicing teachers. The survey asked teachers to consider two competing hypothetical lessons described by a number of rich task dimensions and by the technology used. In the first stage of the experiment, respondents indicated which of the two lessons they were most likely to deliver to their students. They were also asked whether delivering a lesson described in this way would be satisfactory. These types of questions are consistent with other choice studies and provide a summative indicator that can then be modeled to understand which rich characteristics drive overall lesson choices and also to understand the direct and moderating effects of the IWB technology (see Louviere et al., 2000).

In a second stage of the experiment, respondents indicated which of two hypothetical lessons was superior in terms of (a) which would most improve student learning outcomes, (b) which would students enjoy more, and (c) which would be easier to prepare. The first question was included to determine whether teachers’ perceptions of positive learning outcomes are associated with certain rich task characteristics (e.g., Handal & Herrington, 2003) and how technology affects this directly and via a moderating effect. The second question relating to enjoyment was based on findings borne out in the qualitative research component and highlighted by previous research (e.g., Hall & Higgins, 2005). The third question was included to assess how aspects of effort may impact choices, consistent with previous research in teaching (e.g., Ertmer et al., 2012) and studies of ease-of-use (e.g., Burke, 2013; Davis, 1989; Venkatesh & Bala, 2008).

A mathematics lesson was used as the context for this experiment. The lesson was targeted at developing the mathematical concept of Area with Year 5 Australian primary (elementary) school students. As well as the reasons outlined in the background to this study, rich task pedagogies are highlighted as desirable in mathematics curriculum documents and were familiar to teachers participating in the study (Australian Curriculum, Assessment and Reporting Authority [ACARA], 2012; NCTM, 2000). The study of Area is a compulsory component of the curriculum. It was selected for its capacity to be taught in diverse ways, and therefore, it integrated well in our design approach, which varied in the richness of the lesson being considered by respondents.

The DCE varied six selected characteristics of rich tasks, which were derived from the literature on rich tasks (e.g., Education Queensland, 2002; Henningsen & Stein, 1997; Shimizu et al., 2010). The selection of the characteristics was informed by two influential government documents. The first document described characteristics of quality teaching in the Australian state of NSW (NSW DET, 2003b), and the second, the NSW Mathematics K-6 curriculum document (NSW Board of Studies [BOS], 2002), articulated desirable attributes of mathematics teaching. Consequently, teachers were familiar with the six rich task characteristics used in the survey. These characteristics were also selected because each could be varied independently of any other characteristic. The six rich task characteristics used in the DCE were as follows:

(i) authenticity: how well the task provides real-world relevance and personal meaning to the learner (Education Queensland, 2002; Radinsky, Bouillion, Lento, & Gomez, 2001);

(ii) student-led inquiry: the extent to which the task is governed by student generation of own problems to investigate, sufficiently open-ended and complex-to-incorporate multiple-solution pathways, and demanding high levels of analysis and theory-making (Downton et al., 2006; Education Queensland, 2002);

(iii) diversity of resources: the extent to which students use a variety of measuring instruments, materials, and manipulatives to complete the task (Grootenboer, 2009);

(iv) collaboration: the level of group work and collaborative approaches to learning (Grootenboer, 2009; Johnson & Johnson, 1987);

(v) student-generated assessment: how tasks are governed by student direction in assessment in ways that connect naturally with what has been taught (Downton et al., 2006); and

(vi) student reflection: the extent to which tasks encourage reflective practices among students, including processes of description, analysis, and application of learning to new contexts (NSW BOS, 2002; NSW DET, 2003b).

Figure 1 shows a sample scenario from the DCE, which consists of Alternative Lessons A and B. Each characteristic was described using two levels (high or low) of “richness.” Each lesson feature was described in a way to maximize teacher comprehension. Generic labels were deliberately used to further reduce bias associated with their identification as rich task characteristics (e.g., assessment rather than authentic assessment). Within each lesson the level of richness for each characteristic was varied. For example, in the present study, authenticity of the rich task was presented in terms of the students studying the area needed for the placement of stalls (booths) at a school fete. To clarify, a school fete is an outdoor fair often held by Australian schools to raise funds. It comprises stalls or booths at which products are sold or at which participants may undertake an activity or game. Additionally, each lesson included the use of either the IWB or “nonuse” of the IWB (labeled as the “medium”). The teachers were asked to choose a preferred descriptive scenario rather than to choose a definition of rich task characteristics.

Figure 1.

An example of a lesson choice scenario (Stage 1)

Procedure

The DCE was administered to teacher participants using a commercial online panel. Each participant received an email invitation to participate based on his or her employment profile. Respondents to the survey had to answer a sampling question at the start of the survey to indicate they were primary school teachers (teachers of years K-6) to be able to continue with the survey.

Each participant responded to 16 scenarios in two stages. In Stage 1, the first eight scenarios required teachers to nominate the lesson that they would prefer to deliver. In Stage 2, the second set of eight scenarios required teachers to nominate which lessons were easier to prepare, maximized student learning, or maximized student enjoyment. The first stage preceded the second stage so as to avoid any biases that might be induced by privileging the dimensions of preparation, learning, and enjoyment relative to other factors that might be driving their choices. Optimal design theory (Louviere & Woodworth, 1983; Street & Burgess, 2007) was used to determine the total number and content of scenarios, to allow the estimation of main effects relating to the variation of rich task characteristics, as well as to allow the two-way interaction between the rich task characteristic and the use of the IWB. As per our research objectives, the two-way interaction terms allow us to quantify the extent to which each rich task characteristic is moderated in its impact on various outcomes (e.g., perceptions of learning, enjoyment, and preparation; overall preference) by the use of an IWB. There were 64 scenarios in each stage of the experiment. Each teacher was randomly allocated to one of eight subsets in each stage.

Findings

The data were analyzed using discrete choice modeling (Ben-Akiva & Lerman, 1985). Four models predicted the impact of task characteristics and IWB use on (1) lesson chosen, (2) perceived ease of lesson preparation, (3) perceived student learning, and (4) perceived student enjoyment. The independent variables in each model used to predict these outcomes were the six rich task characteristics, the use/nonuse of IWB, and the set of two-way interactions between each rich task characteristic and the use of an IWB. Each model also included five estimates to indicate how responsiveness to the use/nonuse of the IWB varies across six subgroups of teachers. These subgroups represent differences with respect to an individual teacher’s self-reported experience with the IWB technology and his or her current access to an IWB for lesson delivery (Voogt & Knezek, 2008). The parameter estimates for these four models, associated standard errors of each estimate, and indicators of significance of each variable are presented in Table 1.

Table 1

Parameter Estimates of Four Choice Models on Rich Task Characteristics and IWB Use

	Overall Preference			Maximizes Perceived Student Learning			Maximizes Perceived Student Enjoyment			Minimizes Perceived Teacher Preparation
Parameter estimate	β	SE		β	SE		β	SE		β	SE
Authenticity of task	0.1384	(0.0323)	***	0.1592	(0.0322)	***	0.2397	(0.0336)	***	−0.1189	(0.0316)	***
Student-led inquiry	0.0425	(0.0326)	ns	0.1435	(0.032)	***	0.1747	(0.0341)	***	0.1325	(0.0314)	***
Use of collaboration	0.3463	(0.0337)	***	0.2431	(0.0325)	***	0.4616	(0.0354)	***	0.0346	(0.0314)	ns
Resource-intensive	0.2060	(0.032)	***	0.1491	(0.032)	***	0.1676	(0.0345)	***	−0.1397	(0.0316)	***
Student-led assessment	−0.1948	(0.0331)	***	0.0012	(0.0326)	ns	0.1508	(0.0344)	***	0.1293	(0.0316)	***
Reflection on process	0.0613	(0.0322)	*	0.1582	(0.0318)	***	0.0294	(0.033)	ns	0.0648	(0.0316)	**
Use of IWB (aggregate)	−0.0334	(0.0322)	ns	0.1263	(0.0319)	***	0.2465	(0.0336)	***	0.1236	(0.0314)	***
- No access/novice (17%)	−0.0966	(0.0781)	ns	0.0187	(0.0762)	ns	−0.0023	(0.0789)	ns	0.0056	(0.0753)	ns
- Bookable/novice (12%)	−0.1394	(0.0951)	ns	0.2521	(0.0958)	***	0.2586	(0.0989)	***	0.0311	(0.0916)	ns
- Bookable/intermediate (9%)	−0.2223	(0.1073)	**	−0.0258	(0.1036)	ns	0.0396	(0.107)	ns	−0.0977	(0.1036)	ns
- In-class/novice (11%)	−0.0447	(0.0979)	ns	0.1884	(0.0978)	*	0.5046	(0.1084)	***	0.3515	(0.1010)	**
- In-class/intermediate (37%)	0.0368	(0.0533)	ns	0.1759	(0.053)	***	0.3330	(0.0562)	***	0.1462	(0.0520)	**
- In-class/advanced (15%)	0.0785	(0.0845)	ns	0.0874	(0.0829)	ns	0.2872	(0.0874)	***	0.2677	(0.0845)	**
IWB × Task	0.0220	(0.0324)	ns	0.0047	(0.0318)	ns	−0.0529	(0.0334)	ns	0.0485	(0.0314)	ns
IWB × Inquiry	−0.0921	(0.0324)	***	−0.0300	(0.0318)	ns	0.0434	(0.0335)	ns	−0.0402	(0.0315)	ns
IWB × Collaboration	0.0185	(0.0325)	ns	0.0328	(0.0319)	ns	0.0090	(0.0336)	ns	0.0111	(0.0315)	ns
IWB × Resources	−0.0161	(0.0320)	ns	−0.0046	(0.0316)	ns	0.0180	(0.0334)	ns	−0.0172	(0.0312)	ns
IWB × Assessment	0.0308	(0.0317)	ns	0.0068	(0.0313)	ns	0.0197	(0.0329)	ns	−0.0301	(0.0308)	ns
IWB × Reflection	0.0269	(0.0324)	ns	0.0103	(0.0319)	ns	0.0547	(0.0334)	ns	−0.0070	(0.0314)	ns

Note. Standard error of parameter estimates appear in parentheses.

p < .10. **p < .05. ***p < .01.

The first six parameter estimates correspond to the impact that each rich task characteristic has on each of the four choice outcomes. For example, the estimates indicate that rich task authenticity is perceived by teachers to maximize student enjoyment and learning yet involves lessons that are relatively difficult to prepare (Figure 2). The overall preference of teachers is to deliver lessons with higher authenticity. We now consider these results in more detail in terms of how each rich task characteristic impacts on perceptions of learning, enjoyment, and lesson preparation and overall lesson preferences as well as the direct and moderating impact of the IWB technology on these outcomes.

Figure 2.

Impact of variation in authenticity on teacher perception and choice

Impact of Rich Task Characteristics and IWBs on Overall Preference

Overall, teachers preferred tasks that were high in authenticity (p = .0000), involved students working in groups (i.e., collaboration) (p = .0000), used a variety of resources (p = .0000), and used assessment tasks that were designed by themselves rather than led by students (p = .0000). Those parameter estimates that are larger in absolute value indicate the corresponding rich task factor has a greater relative impact on overall choice. In this case, the variation in the use of collaboration in lessons (β = .3463) was the strongest factor in determining teachers’ choices. The type of student reflection—namely, whether students present a report to describe findings or whether the report asks students to reflect on the process used and possible improvements—had only a marginal effect that was significant at the 10% level (p = .0572).

At the aggregate level, the use or nonuse of an IWB did not have a significant impact on teachers’ overall lesson preference (p = .3001). However, this result is largely driven by differences in teachers’ accessibility and knowledge of the IWB technology. Specifically, teachers who currently have limited access (i.e., via a booking process) or no access to an IWB in the classroom have a significant preference for lessons that do not involve the use of the IWB (p = .0379). This is in contrast to those teachers with access to an IWB holding intermediate or advanced knowledge of the IWB who significantly prefer lessons in which the IWB is incorporated. The preference for a non-IWB lesson is most pronounced by the 9% of teachers in the sample who must book an IWB to use it in their lessons and those who describe themselves as having an intermediate level of experience in using this technology.

The use or nonuse of IWBs had no impact in moderating the manner in which teachers were influenced by rich task characteristics, other than for student-led inquiry. Specifically, the overall impact of student-led inquiry appears to be insignificant in determining teachers’ preferences (p = .1923). However, the presence of a two-way interaction between student-led inquiry and the use and nonuse of IWBs was significant (p = .0044). This result suggests that when lessons involved student-led inquiry, teachers expressed a significant preference for not using the IWB over using it (see Figure 3). In addition, when the IWB is not used, student-led inquiry was significantly preferred to teacher-led inquiry.

Figure 3.

Overall preference for student-led inquiry over teacher-directed lesson

Impact of Rich Task Characteristics and IWBs on Student Learning and Enjoyment

Teachers have similar perceptions about the impact that rich task characteristics will have on student learning and student enjoyment. Authenticity, student-led inquiry, collaboration, and use of varied resources were all associated with maximizing student learning and enjoyment. Again, the use of collaborative group work appears to be a dominant aspect of teachers’ evaluation of those factors that most impact student learning (β = .2431) and enjoyment (β = .4616). This implies that if two lessons were identical but varied only in terms of whether students worked individually or in groups, 62% and 71% of teachers would indicate that the lesson involving group work maximizes student learning and student enjoyment, respectively. Also, student-led assessment was perceived by teachers to maximize student enjoyment (p = .0000) but did not significantly impact student learning (p = .9696). In contrast, student reflection was perceived by teachers to maximize student learning (p = .0000) but did not significantly impact student enjoyment (p = .3731).

Overall, teachers indicated that they perceived IWB use to have a positive impact on both student learning (p = .0010) and student enjoyment (p = .0000). The perceived positive impact of IWBs on student learning and enjoyment is predominantly driven by the 67% of teachers in the sample who currently teach in a classroom already equipped with an IWB. This perception is similarly shared by teachers who can access an IWB through a booking process and describe themselves as having no or only introductory experience in using the IWB. Those without current access to an IWB indicated no perceived difference in the impact the technology has on student learning.

The use or nonuse of IWBs did not moderate the impact that individual rich task characteristics have on perceived levels of student learning or enjoyment. For example, regardless of whether an IWB was used or not, teachers indicated a perception that collaboration would positively impact student learning and enjoyment.

Impact of Rich Task Characteristics and IWBs on Teacher Preparation

Teachers’ choices indicate that they believed student-led inquiry (p = .0000), student-led assessment (p = .0000), and student reflection (p = .0400) were all associated with lessons that were easier to prepare. In contrast, teacher preparation was perceived to be more difficult for lessons with higher authenticity (p = .0002) and a larger variety of resources (p = .0000). Teachers did not perceive any significant difference in preparation between lessons that involved students working individually or in groups (p = .2705).

The use or nonuse of an IWB did not moderate the perceptions about how rich task characteristics impact the ease of lesson preparation. The use of an IWB, however, was associated with easier lesson preparation (p = .0009). This effect, however, was entirely dictated by the majority of teachers (67%) who currently have an IWB in their classroom. Furthermore, this perceived ease of preparation associated with lessons involving the IWB technology among this group of teachers was not affected by differences in level of experience in using the technology.

Correlation Between Overall Preference, Perceptions of Student Learning, Student Enjoyment, and Ease of Lesson Preparation

The level of correlation between each of the parameter estimates reveals the manner in which the impact of rich task characteristics on overall preference is associated with student learning, enjoyment, and ease of preparation. In particular, rich task characteristics that teachers perceive to have a positive impact on student learning are positively correlated with overall lesson preferences (r = .7657). Similarly, rich task characteristics that have a positive impact on student enjoyment also appear to be positively correlated with overall lesson preferences (r = .5597). In contrast, teachers’ overall preferences were negatively correlated in terms of how they believe rich task characteristics impacted ease of lesson preparation (r = –.4259). Together, these results imply that although some characteristics of rich tasks require greater levels of lesson preparation, they are more likely to be preferable to teachers because they maximize student learning and enjoyment. For example, when a rich task requires a greater variety of resources, the preparation is more difficult, but teachers express a strong overall preference for such lessons and indicate that these will contribute to higher levels of student learning and enjoyment (see Figure 4).

Figure 4.

Impact of resources on perceived enjoyment, learning, lesson preparation, and lesson choice

Discussion

The findings show that teachers preferred lessons that exhibited high levels of rich learning task characteristics. Regardless of whether an IWB is to be used or not, teachers prefer to teach lessons that are authentic and where students work collaboratively using highly varied resources. However, teachers prefer to take the lead in assessing student learning rather than allowing students to control their own assessment. The preference for teacher-directed assessment was evident despite beliefs that students would enjoy designing their own questions and despite their belief that lessons involving student-generated questions and student-designed tests are easier to prepare. Teachers’ preferences for rich task pedagogy are generally consistent with policy recommendations suggesting the adoption of rich tasks to promote student learning (e.g., Education Queensland, 2002).

The expectation that the use of technologies, such as IWBs, would influence the nature of teachers’ pedagogical decisions was, in general, not borne out in the research findings. The discrete choice model revealed that the use or nonuse of IWB generally did not moderate teachers’ decision-making processes about rich task pedagogy. In only one aspect of rich task pedagogy did the IWB moderate overall teacher preferences. Specifically, teachers expressed a significant preference for student-led inquiry over teacher-directed lessons when the IWB was not used. When the IWB was used, however, teachers expressed indifference between teacher-directed lessons over student-led inquiry. This is despite teachers indicating that they believed student-generated questions and inquiry were more likely to provide improved learning outcomes, greater student enjoyment, and ease of preparation. The IWB was perceived by teachers to be better suited for demonstrating a known and tested way to solve a problem, but by contrast was not well suited for students to provide their own solutions to the same problem. This is consistent with the literature that suggests IWBs require teachers to orchestrate the affordances and constraints of the setting, with attention to student characteristics and pedagogical goals (Kennewell & Beauchamp, 2007). Overall, with only one element of rich task learning being moderated by its use in regards to the suitability of student-led inquiry over teacher-directed learning, the findings provide further evidence that IWBs are not a strongly disruptive technology (Hedberg, 2006).

Student-led assessment and student-led inquiry were associated with perceived ease of lesson preparation and high levels of student enjoyment, and student-led inquiry was associated with high levels of perceived student learning. Yet these two characteristics (student-led assessment and student-led inquiry) were the only rich task features not generally favored by teachers in their overall preferences. The reasons for this are unclear. It may indicate a lack of confidence with student-led approaches or simply a fundamental preference to maintain teacher control. Other explanations may be that student inquiry does not align well with curriculum demands or high-stakes assessment. Further research is needed here.

The use of the DCE allowed the investigation of the relative impact of rich task characteristics on learning, enjoyment, preparation, and overall preference. This is in contrast to other methods that may examine the impact of these characteristics in isolation or in highly correlated environments where relative impact cannot be learned. This research revealed that teachers valued student collaboration more highly than other features of rich task pedagogy and that this characteristic was perceived as having the most impact on all dimensions of learning, enjoyment, preparation, and overall preference. This perception strongly supports the results of research in this area conducted a few decades ago (Damon & Phelps, 1989; Johnson & Johnson, 1987; Jonassen, Peck, & Wilson, 1999; Rogoff, 1990). Indeed, student collaboration was emphasized by most teachers in their overall lesson preferences, regardless of IWB use; in other words, the technology does not necessarily modify overarching learning objectives and strategies: In some situations, the traditional medium may be more appropriate or effectively used in combination with technologies such as IWBs (Kennewell & Beauchamp, 2007).

In general, the impact of rich task characteristics on teachers’ lesson choices is highly correlated with the impact that these same characteristics have on perceptions of student learning and enjoyment, rather than ease of preparation. Indeed, those lessons that appear to result in higher levels of preparation were often associated with lessons that teachers preferred to teach. It is possible that teachers generate their own satisfaction, reward, and achievement by choosing those lessons perceived as creating higher levels of student enjoyment and learning. This suggests that researchers examining questions of teaching choice need to further investigate the intrinsic rewards that teachers themselves may experience in the classroom.

The primary purpose of the research was to investigate how rich task characteristics affect overall lesson choices and teacher perceptions regarding student learning, student enjoyment, and ease of lesson preparation. We also examined whether these perceptions and preferences are moderated by the IWB technology. However, the research outcomes also indicate how the IWB technology itself is valued in different ways across subsets of teachers. In particular, those with limited access to the technology prefer to conduct lessons in ways that avoid the IWB. On the other hand, those teachers with immediate access to IWBs in their classrooms perceive the technology can reduce the burden on lesson preparation, while maximizing student learning and enjoyment, regardless of teachers’ levels of experience with the IWB. These findings are largely consistent with how technology integration is dictated by external first-order barriers related to accessibility (e.g., Ertmer et al., 2012). This study was conducted in the context of a primary mathematics lesson. Further research is required to explore the generalizability of these findings. In this study, set in a primary mathematics context, it was not possible to investigate every feature that has been identified as characteristic of rich tasks. In future studies, pluridisciplinarity, as a key feature of rich tasks, should warrant investigation to test for interactions with teacher choices of pedagogies and the ways in which they decide to employ learning technologies.

Conclusion

A challenge for education is to develop and facilitate the use of effective pedagogies based on rigorous evidence of how they contribute to quality learning. The adoption of rich tasks has been encouraged by curriculum policies. However, teachers can be selective in those rich task elements to adopt in their own practice. Underlying such preferences, teachers may have established their own beliefs about those elements of rich task learning that are likely to create greater levels of student learning outcomes relative to others. Also, teachers must balance their choices against competing considerations in terms of other factors, such as how each rich task element may aid or compromise student enjoyment or induce greater levels of preparation.

Teaching is a complex activity, and teachers’ choices at the technology–pedagogy interface are determined by a wide variety of factors. Researchers and policymakers need to understand the relationship between teacher preferences, pedagogy, and educational technologies to ensure quality teaching and learning. The set of DCEs used in this study permitted the investigation of relationships among rich task characteristics, perceptions of student learning, student enjoyment and ease of lesson preparation, and technology use. The DCE provided quantitative evidence that teachers have strong alignments with rich task pedagogy that are independent of the technology they plan to use.

Footnotes

Authors

PETER AUBUSSON, PhD, is a professor of education in the Centre for Research in Learning and Change at the University of Technology, Sydney, P.O. Box 222, Lindfield, NSW 2070, Australia; peter.aubusson@uts.edu.au . His research focuses on education futures, teacher professional learning, use of analogy in teaching, technology-enhanced learning, and science learning and teaching.

PAUL BURKE, PhD, is a senior research fellow at the Centre for Study of Choice (CenSoC), University of Technology, Sydney (UTS); paul.burke@uts.edu.au . His research focuses on choice modelling and its application in a number of domains including teacher education, ethical consumerism, and usability of technology.

SANDY SCHUCK, PhD, is a professor of education in the Centre for Research in Learning and Change at the University of Technology, Sydney, P.O. Box 222, Lindfield, NSW 2070, Australia; sandy.schuck@uts.edu.au . Her research focuses on education futures, teacher professional learning, technology-enhanced learning, and mathematics learning and teaching.

MATTHEW KEARNEY, PhD, is an associate professor in the area of ICT in education. He is a member of the Centre for Research in Learning and Change at the University of Technology, Sydney. His research interests are in the area of technology-enhanced learning in school and teacher education contexts. matthew.kearney@uts.edu.au

BART FRISCHKNECHT, PhD, is a senior research fellow at the Centre for the Study of Choice in the Business School at the University of Technology, Sydney, Australia; bart.frischknecht@uts.edu.au . He leads novel academic and industrial projects that identify strategic growth opportunities, describe a firm’s competitive position, forecast product demand, and combine engineering and market research for new product development.

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