Figure 1. The BBIL model’s structure and features (numbers of features refer to their description in the text).
Adi Kidron* and Yael Kali
Technologies in Education Program, Faculty of Education, University of Haifa, Haifa, Israel
Abstract
The purpose of this work is to contribute to the body of knowledge on processes by which students develop interdisciplinary understanding of contents, as well as to suggest technology-enhanced means for supporting them in these processes in the context of higher education. In doing so, we suggest a rethinking of three traditional practices that tend to characterise typical higher education instruction: (1) compartmentalisation of disciplines; (2) traditional pedagogy; and (3) traditional hierarchies based on levels of expertise. Our high-level conjecture was that meaningful dialogue with peers and experts supports both the deepening of ideas in one knowledge domain and the formation of connections between ideas from several domains, both of which are required for the development of interdisciplinary understanding. We developed the Boundary Breaking for Interdisciplinary Learning (BBIL) model, which harnesses technology to break boundaries between disciplines, learners and organisational levels of hierarchy. Findings indicate that 36 undergraduate students who participated in an interdisciplinary online course that implemented the BBIL model have significantly improved their interdisciplinary understanding of the course contents. This study illustrates how innovative use of available, free and low-cost technology can produce a ‘positive disruption’ in higher education instruction.
Keywords: interdisciplinary learning; interdisciplinary understanding; learning community; cognitive apprenticeship; technology-enhanced learning; instructional design; higher education; online education
Citation: Research in Learning Technology 2015, 23: 26496 - http://dx.doi.org/10.3402/rlt.v23.26496
Responsible Editor: Carlo Perrotta, University of Leeds, United Kingdom.
Copyright: © 2015 A. Kidron and Y. Kali. Research in Learning Technology is the journal of the Association for Learning Technology (ALT), a UK-based professional and scholarly society and membership organisation. ALT is registered charity number 1063519. http://www.alt.ac.uk/. This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 International License, allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.
Received: 31 October 2014; Accepted: 27 September 2015; Published: 28 October 2015
*Correspondence to: Email: adi.kidron@edtech.haifa.ac.il
The 21st century and the ‘knowledge revolution’ pose challenges that demand different ways of thinking and the development of new skills. One of the critical skills is the ability to think and integrate knowledge across disciplines and to understand the relations between fields of knowledge (Frodeman 2010). Developing such an interdisciplinary understanding requires a learning process through which learners integrate insights and modes of thinking from a number of disciplines to advance their understanding of a topic which is beyond the scope of a single discipline. Boix-Mansilla (2010) refers to such a learning process as interdisciplinary learning. But when we turn to higher education institutions, as key players in preparing young people to cope with the challenges that this century poses, we find that although there are theories and pedagogical approaches that have the potential to promote interdisciplinary learning, it seems that current academic organisational structures are typically geared towards instruction that compartmentalises disciplines, instead of providing students with the tools for integrating knowledge (Salomon 1991). In fact, it is argued (e.g. by Christensen et al. 2011) that colleges and universities are in the midst of a complex crisis and are, therefore, expected to rethink their traditional goals and practices, in the face of competition from newer alternatives such as online education. Taking a disruptive innovation stance, one should ‘rethink the age-old assumptions about higher education’ (Christensen et al. 2011, p. 4). The current work suggests a rethinking of three traditional practices that tend to characterise typical higher-education instruction: (1) compartmentalisation of disciplines; (2) traditional pedagogy; and (3) traditional hierarchies based on levels of expertise.
The purpose of this work is to contribute to the body of knowledge that explains the processes by which students develop interdisciplinary understanding of contents, as well as to suggest technology-enhanced means for supporting students in these processes in the context of higher education. Our high-level conjecture (Sandoval 2014) is that interdisciplinary understanding entails a deep understanding of disciplinary ideas, simultaneously combined with the ability to see connections between different disciplinary ideas in several domains, and that these abilities are gained through meaningful dialogue and exposure to a diversity of ideas and ways of thinking.
In order to promote interdisciplinary understanding, we developed the Boundary Breaking for Interdisciplinary Learning (BBIL) model, which harnesses technology to address the limitations described above regarding compartmentalisation, traditional pedagogy and organisational hierarchies.
The BBIL model refers to three perspectives:
This work focuses on the learning experience, as perceived by students who participated in academic courses in which the generic model was implemented, and on their learning outcomes with respect to interdisciplinary understanding.
In accordance with Thompson-Klein’s (2010) taxonomy, we refer to ‘breaking boundaries between disciplines’ as interdisciplinarity, rather than multidisciplinarity. The main difference is that interdisciplinarity integrates various disciplinary perspectives to create new integrative knowledge, whereas multidisciplinarity combines disciplinary perspectives but with minimum interactions, while maintaining the identity of each discipline and its knowledge structures.
In order to explore the notion of interdisciplinarity, this work adopts the theoretical framework of Interdisciplinary Learning as a Pragmatic Constructionist View (ILPCV) (Boix-Mansilla 2010). Within this framework, interdisciplinary learning is a process by which learners integrate information, data, techniques, tools, perspectives, ideas, concepts and theories from two or more disciplines, to create products, explain phenomena, or solve problems in ways that would have been unlikely through single-disciplinary means. ILPCV describes four main cognitive processes by which learners integrate ideas from different disciplines and gradually develop interdisciplinary understanding:
Another framework for exploring the integration of ideas is Knowledge Integration (KI) (Linn and Eylon 2011) which offers a conceptual and practical lens for understanding KI processes, for evaluating them and for supporting their emergence using instructional and design principles. KI focuses on the personal repertoire of ideas that students develop as a result of their learning experiences. By processing and creating links between these ideas, students build a coherent and normative understanding that enables them to interpret new situations. Linn and Eylon (2011) claim that these processes can be supported by eliciting students’ ideas; adding new, pivotal ideas; developing criteria for distinguishing among ideas; and sorting out ideas.
As an educational approach, learning communities emphasises the social-cultural aspects of learning and the advancement of collective knowledge as a means for individual learning. A core characteristic of a learning community is the diversity of expertise among its members, who are valued for their contributions and are given support for personal growth and development (Bielaczyc, Kapur, and Collins 2013). The culture of a learning community encourages all participants to express their unique voices, as they bring their personal background and heritage into the discussion. Such a culture is gradually cultivated while breaking boundaries between individual learners. Technology has been shown to have an added value in supporting these processes (Kali, Levin-Peled, and Dori 2009; Scardamalia and Bereiter 1994). Hoadley (2012) describes three types of technology affordances for deploying or designing environments for communities. These include:
When considering learning within a community that brings together learners with different levels of expertise, the notion of cognitive apprenticeship (Collins 2006) becomes very relevant. The goal of cognitive apprenticeship, as a pedagogical approach, is to assist novices in gaining mastery in a certain skill or concept. The learning occurs in a natural context that involves both novices and experts, and is based on several core processes:
The effectiveness of cognitive apprenticeship depends on the novices’ ability to participate in the practices of their community. Lave and Wenger (1991) described this process as ‘legitimate peripheral participation’, in which novice members first practice peripheral tasks that do not require high levels of expertise, but enable them to enculturate and get familiar with the community’s ways of thinking, doing and communicating. Nevertheless, these tasks, albeit peripheral, are necessary for the community’s ongoing practice, and hence make the novices legitimate members. As the learning progresses, the novices gain expertise, practice core tasks and become more central members of the community.
The use of technology has been proven to augment cognitive apprenticeship processes and improve students’ performance, as described in the work of Kopcha and Alger (2014) on Technology-Enhanced Cognitive Apprenticeship (TECA), which uses technology to enhance the processes, content and social aspects of cognitive apprenticeship. The researchers conclude that the TECA approach may be more beneficial when it coordinates multiple technologies that not only integrate key content with guidance and support for a variety of apprenticeship activities (e.g. modelling, coaching, scaffolding, reflecting), but also promote discussion of learning experiences in a larger community. And yet, as Hoadley (2012) describes, the notion of providing learners with access to experts, and the legitimacy for peripheral participation, stand in strong contradiction with the ways students are segmented into grades or levels within educational institutions. Furthermore, the traditional lecture format usually leaves no opportunity for students to participate in any practice related to the knowledge at hand. Thus implementing the notion of Breaking Boundaries between Organisational Hierarchies is likely to be a challenging task.
Connections between the above notions form the theoretical grounds for our generic model – a technology-enhanced learning community with varied levels of expertise can provide a rich environment for links to emerge between disciplines and for interdisciplinary understanding to develop among learners at all levels of expertise. Based on the three perspectives reviewed above, we defined the following ‘pragmatic design principles’ (Kali 2008), which we view as core principles for supporting interdisciplinary learning in higher education:
The BBIL model is a generic model developed in this study and derived from the above pragmatic design principles. Technology plays a crucial role in embodying (Sandoval 2014) these design principles into the model’s features; however, we only used available technologies, since we view the innovation of the BBIL model not in its cutting-edge technology, but rather in its conceptual design. Below, we describe the BBIL model and illustrate how we employed it for designing a set of two technology-enhanced semester-long higher education courses (undergraduate and graduate levels), in which students studied similar interdisciplinary contents adapted to their level of expertise. The courses also shared a similar title – Learning in a NetworKed Society (LINKS) – and involved six disciplinary knowledge domains each: learning sciences, science-communication, health sciences, cognition sciences, media and communication, and information sciences. The undergraduate course was taught fully online (learning environment developed in Moodle with embedded Google Docs), while the graduate course was taught in a hybrid format, with weekly meetings and online assignments (Google Sites with embedded Google Docs). Both courses were taught simultaneously (by the authors of this article) to enable interactions between them, as explained below.
Figure 1 illustrates the general structure of the two courses with a zoom-in into the technology-enhanced features used to create interactions between the two courses and the features designed for the learning of each disciplinary domain.
Figure 1.
The BBIL model’s structure and features (numbers of features refer to their description in the text).
To implement the design principle of breaking boundaries between disciplines, the BBIL model includes the following design features:
This article reports our findings from an implementation of the two LINKS courses in the Spring semester of 2013, at one of the universities in Israel. We focused our analysis on the learning processes of students from the undergraduate course. The rationale for this decision was that unlike graduate courses, typical undergraduate courses at that university rarely use a learning community approach. Therefore, we assumed that the impact of our model on undergraduate students would be stronger and that their learning processes would be more salient and characterisable.
Figure 2.
A screenshot taken from an ‘Appetiser’ activity (names have been changed for anonymity).
For methodological purposes, we define interdisciplinary understanding, operationally, as the ability to explain a cross-cutting theme in a coherent and logical way, using arguments that integrate knowledge from different disciplinary domains. Specifically, this study sought to answer two complementary questions:
Given the coupling of theoretical and design objectives in this research, a design-based research methodological approach was chosen. This approach was developed to enable systematic examination of learning processes that occur in real-world settings (such as classrooms), that are mediated through the use of environments (often enhanced by technology) which have been designed to promote learning, as well as our understanding of the learning processes (Kali 2008; Collins, Joseph, and Bielaczyc 2004). Design-based research often uses mixed-method approaches to characterise learning processes that occur in innovative environments, as well as to produce generalisations, using a combination of quantitative and qualitative means (Chi 1997).
We used conjecture mapping – a technique for representing conjectures in design-based research (Sandoval 2014), for representing the different conjectures that initiated this work (high-level conjectures); influenced the design (design conjectures); and supported the definition of the research questions and conclusions (theoretical conjectures). Figure 3 illustrates the constructs of a generalised conjecture map, as described by Sandoval.
Figure 3.
Generalised conjecture map (adapted from Sandoval 2014, in Kali, Sagy, Kuflik, Mogilevsky, and Maayan-Fanar 2015).
Figure 4 illustrates the mapping of the conjectures of the current study using Sandoval’s (2014) conjecture mapping technique.
Figure 4.
Conjecture mapping of the current research using Sandoval’s (2014) technique.
Thirty-six undergraduate students from the Faculty of Education chose the course as part of their elective requirements. Thirty-four students completed the course (a dropout rate of 5.5%). Students varied in their academic background (e.g. theatre studies, history, music, psychology, literature, law, languages, communication, education, environmental studies) and had different professional experiences (teachers, social workers, lawyers, instructional designers). Previous experience with online courses varied from students with no experience at all, to students who had already participated in one or more online courses.
In order to evaluate the development of interdisciplinary understanding, data was collected through a task that was assigned twice, once in the middle of the course and once at the end. Students were required to choose one of several suggested questions regarding the cross-cutting theme and answer it individually in a 1,000-word essay, in which they were supposed to integrate ideas from the three disciplinary domains taught in the course (in the first assignment, this referred to the first three domains, whereas the final assignment referred to the other three). For instance, one of the questions was articulated as follows: ‘In light of the three disciplinary domains recently presented in the course, what are the roles of teachers and students in a networked society, and how are they different from more traditional roles?’. All 34 students who completed the undergraduate course submitted the two essays (100% of the participants).
To shed light on the learning processes, as perceived by students, we presented a questionnaire at the end of the course with five open-ended questions that asked students about the course’s contribution; the connection between the cross-cutting theme and the disciplinary contents; the added value of collaborative learning (as compared to individual learning); and their most and least meaningful design features. We also included one Likert-type question (‘How would you describe your general learning experience in the course?’) to assess the students’ general satisfaction on a 1 (‘very good’) to 5 (‘disappointing’) scale. Of the 34 students in the course, 26 answered the questionnaire. The answers to the open question regarding the course contribution were detailed and, thus, provided rich data for analysis. Therefore, we segmented answers into statements that indicated various emergent themes. This resulted in 76 statements (an average of three statements per student), that were analysed, as described in the analysis section below.
Our assessment of interdisciplinary understanding was inspired by the KI framework (Linn and Eylon 2011). The original rubric is a 1–5 scale that assesses KI based on the number and quality of links between ideas, which students expressed in short answers they wrote in response to questions in specific knowledge domains.
Certain adaptations were made to the original rubric, which referred to the following issues:
Using the Adapted KI Rubric, two judges separately evaluated the mid-term and end-of-course assignments of all students. Each assignment was graded on a 1–3 scale in reference to three criteria: (1) Disciplinary grounding (profound and accurate understanding of the disciplinary ideas); (2) Integration (analysis of each disciplinary domain in light of the synthesis question, integration of the disciplinary ideas into a summarising answer); and (3) Quality of writing (coherency). After agreeing upon the score of each of the essays, only the integration scores were normalised to a 0–100 scale, and an average mean integration score for each assignment was calculated. To compare the integration mean score of the mid-course assignment to the integration mean score of the final assignment, a paired-samples t-test was conducted.
Students’ answers to the open-ended question were analysed using Chi’s (1997) approach for quantifying qualitative analyses of verbal data. This approach includes four main stages. In the first stage (data volume reduction), we read all the answers and decided to focus on the analysis of one general question (‘What did you gain from the course?’), in which students described their experience in their own words and expressed ideas that enabled us to infer about the mediating processes (Figure 4). Then, in the second stage (data segmentation), we decided that our unit of analysis would be a statement representing one main theme within a student’s answer. Most of the answers included more than one theme. For instance, ‘The course expanded my options of personal independent learning (categorised as belonging to the Added Value of Technology theme) and allowed me to experience thorough learning, collaborative learning and online interactions (the Community Culture theme), and enriched my knowledge allowing me to learn new topics (Knowledge and Skills theme)’. In the third stage (data coding), we developed the coding scheme presented in Table 1, based on emergent themes from the previous stage. Two judges separately used it for coding all of the students’ answers. An agreement of 88% was reached when coded independently and 100% agreement following discussion. Finally, in the fourth stage (pattern interpretation), we calculated the percentages of students whose statements have been coded for each theme.
| Emergent theme from students’ answers to the question ‘What did you gain from the course?’ | Percentage of students who referred to the themea | Example statements |
| (1) Knowledge and skills gained in the course
Course described as new, diverse, interesting and relevant topics; critical thinking, writing skills, time management, distance learning skills |
73% (19/26) | ‘The course topics were very interesting and appealing and encourage me to continue reading and exploring them’. |
| (2) Disruptive nature of the course model
Course described as unique and unusual that changed students’ perceptions about academic learning |
50% (13/26) 12 positive statements; one negative statement | ‘The main contribution … is the change in the way of learning. I realized there are other ways to teach an academic course. The course topics were taught in a way that made them interesting and meaningful for me’. |
| (3) Added value of technology
Claims reflecting positive changes in attitudes towards technology and its potential in educational contexts; claims regarding contribution of technology to self-regulation and intrinsic motivation |
42% (11/26) | ‘At the beginning of the course I was very critical about online learning, but at the end I could see the advantages of such learning, and suddenly it was difficult to find any drawbacks’. ‘I developed responsibility to track the course’s lectures and assignments by myself’. |
| (4) The community culture as a contribution to learning
Participating in a discussion, listening to other opinions, expressing opinions; social interactions; learning with others, exchanging ideas, cross-fertilisation |
27% (7/26) | ‘[I learned] how to accept different ideas and other opinions which may be opposed to mine, how to articulate my own responses in a right way so that others would be able to accept them’. |
| (5) The cognitive apprenticeship approach as an asset
Learning from the graduate course’s products, giving and receiving feedback |
12% (3/26) | ‘My personal mentor [from the graduate student community] contributed a lot to the feeling that my responses are being viewed and responded to’. |
Students’ interdisciplinary understanding of the LINKS contents improved significantly; the t-test indicated a significant difference [t(31)=2.96, p<0.01] in students’ KI scores between the assays that they had written for the mid-course assignment (M=67.2, SD=29.4) and those they wrote for the final assignment (M=82.5, SD=22.0).
Students rated highly their learning experience in the course, as indicated from the Likert-type question in the questionnaire (M=4.4, SD = 0.69). Five main themes emerged from the analysis of students’ answers to the open-ended question (Table 1). As represented in the table, though not directly asked about these themes, 73% of the students mentioned different aspects of the Knowledge and Skills theme, which they felt they had gained in the course; 50% of the students mentioned ideas that indicated their view of the course as disruptive due to its pedagogical and technological nature; 42% indicated the added value of technology; 27% pointed out that the culture of the learning community had been meaningful to their learning; and finally, 12% referred to the interactions with the graduate course students as an asset.
This work was initiated with our high-level conjecture (Sandoval 2014) that meaningful dialogue with peers and experts supports both a deepening into ideas in one knowledge domain and developing connections between ideas from several domains, which are both required for the development of interdisciplinary understanding. We assumed that the affordances of technology would support and enable these processes.
Our first research question focused on the intervention outcomes and sought to explore the extent to which interdisciplinary understanding can be developed and improved. The analysis and comparison between final and mid-term essays indicates that students who participated in the LINKS undergraduate course had significantly improved their interdisciplinary understanding of the cross-cutting theme. This theme – Learning in a Networked Society – which was also the topic of the course, was the main notion that we sought for students to develop their interdisciplinary understanding about. Thus, this research illustrates how careful design, based on theoretically grounded design principles and embodied through a technology-enhanced learning environment, can lead to interdisciplinary understanding, despite the challenges of supporting students in achieving such understanding, as described in the literature (e.g. Boix-Mansilla 2010). We argue that technology, and the unique ways in which it was employed in conjunction with the design principles of the BBIL model, had played a crucial role in this achievement. This claim is supported, as we illustrate below, by the way students perceived their learning experience.
Our second research question sought to explore the way in which students perceived the learning experience, in order to shed light on the mediating processes (Sandoval 2014) that had enabled this significant improvement. We argue, as explained below, that each of the boundary-breaking design principles, as they had been embodied through technological features in the LINKS courses, had contributed to these mediating processes. In fact, we view ‘boundary-breaking’ as a mindset that liberates thinking and promotes mutual growth and cross-fertilisation.
The strongest theme emerging from students’ answers to the open-ended question regarding the contribution of the course was the gaining of deep diverse knowledge and important skills (Emergent Theme #1). This finding becomes even stronger given the potential pitfall of shallowness or confusion when interdisciplinary instruction is implemented (Boix-Mansilla 2010). We believe that the features that were designed to break boundaries between disciplines – such as the Cross-Cutting Theme and the Theme Lens – created a framework into which students could integrate disciplinary ideas coherently and, therefore, develop deep understanding, as described in our mediating processes.
Regarding the breaking of boundaries between learners, an interesting finding is that the collaborative learning processes (categorised as Learning Culture in Emergent Theme #4, e.g. exchanging ideas, exposure to a variety of ideas) seems to have had a meaningful contribution to the development of interdisciplinary understanding. We interpret these as representing one of the other mediating processes – meaningful dialogue and sharing of ideas – and argue that these processes are highly dependent on the technological affordances that were available to the students (e.g. overcoming time and distance gaps, co-constructing knowledge, sharing and recording personal ideas, revisiting discussions).
To implement our third boundary-breaking principle – between levels of hierarchy – we designed interactions between the undergraduate and graduate courses, which could only be enacted using technology-enhanced features. We conjectured that these would support another mediating process – modelling and coaching between the communities. The relatively low percentage of references that students from the undergraduate course made to these interactions (categorised as Cognitive Apprenticeship in Table 1) remains unexplained. A possible explanation is that this resulted from the phrasing of the question (which referred to the contribution of the course and not specifically to the processes and features that led to that contribution). Another possible explanation is that students might not have been aware of the impact of the cognitive apprenticeship’s features on their learning. Either way, it seems that the set of technology-enhanced features that employed this design principle had not been sufficient to meaningfully support this expected mediating process and should be improved and further studied in future implementations.
A most encouraging finding was the students’ perceptions of the disruptive nature of their learning experience in the course. Although students were asked about what they gained from the course (and not what they thought about it in comparison to other courses), 50% of them chose to include statements that expressed the uniqueness of the model in comparison to their usual learning experience. In addition, the findings indicate that technology had played a crucial role in creating this positive disruption: Many of the students (42%) positively changed their attitudes and came to recognise the added value of technology in promoting learning in general, and aspects such as self-regulation and motivation, in particular, through the distant-learning experience provided by the course. Combined with students’ high ratings of their general experience in the course (indicated by their answers to the Likert question), we interpret these findings as indicating that the model, as a whole, and the use of technology had succeeded in creating a very positive disruption in the way students perceived learning in higher education.
With regards to the role of technology, we argue that it played a critical role in implementing our model. Many of the features designed for the course could not have been realised, or would have been very difficult to implement, without the use of technology. This includes features from all of the three boundary-breaking principles: the video-recorded lectures that served as disciplinary resources (Feature #4); the whole-class simultaneous editing of collaborative documents that served for knowledge-building activities (#7); the reuse of these documents for developing modelling artefacts (#8); the tracking of versions and text changes in documents for the peer-review activities (#9); the streamlining of modelling artefacts using collaborative documents shared by students in the two courses (#13); the online forms for structured feedback activities between communities (#14); and the coaching that was provided by enabling graduate students for intervening in the undergraduate course’s online discussions (#15). Thus, this study illustrates that the heart of innovation in educational technology is not necessarily dependent on cutting-edge technology. Innovative use of available, free and low-cost technology is the key to introducing disruptive learning experiences into higher education.
Finally, going back to the challenges posed by Christensen et al. (2011), we argue that this work exemplifies a solid educational basis for rethinking traditional goals, practices, essence and purpose of teaching in higher education. Though this work describes a particular implementation within specific disciplinary domains, the BBIL model has been designed as a generic model, so that its principles can be adopted and implemented in any set of disciplinary domains. We believe that the positive disruption that it created in the current study can be reproduced and thus, redefine the quality of education in other higher education terrains.
This research was supported by the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation grant 1716/12. The authors would like to thank Shai Spieler and his team from the University of Haifa eLearning Support Unit for their help and support.
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