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Abuhassna, H.;  Alnawajha, S. The Transactional Distance Theory and Distance Learning Contexts. Encyclopedia. Available online: https://encyclopedia.pub/entry/41053 (accessed on 03 July 2024).
Abuhassna H,  Alnawajha S. The Transactional Distance Theory and Distance Learning Contexts. Encyclopedia. Available at: https://encyclopedia.pub/entry/41053. Accessed July 03, 2024.
Abuhassna, Hassan, Samer Alnawajha. "The Transactional Distance Theory and Distance Learning Contexts" Encyclopedia, https://encyclopedia.pub/entry/41053 (accessed July 03, 2024).
Abuhassna, H., & Alnawajha, S. (2023, February 09). The Transactional Distance Theory and Distance Learning Contexts. In Encyclopedia. https://encyclopedia.pub/entry/41053
Abuhassna, Hassan and Samer Alnawajha. "The Transactional Distance Theory and Distance Learning Contexts." Encyclopedia. Web. 09 February, 2023.
The Transactional Distance Theory and Distance Learning Contexts
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This entry is adapted from the peer-reviewed paper 10.3390/educsci13020112

Moore established transactional distance theory (TDT) to grasp transactional distance in the context of distance learning. Research using TDT in distance, open, and online learning environments has been undertaken. However, there are information gaps about what constitutes progress, future directions, and research deficits pertaining to TDT in the context of distance education. TDT research in distance learning integrates various theories and models; nevertheless, there is a movement toward acceptance models and how to incorporate more relevant theories within the framework of distance learning. Future studies should integrate other aspects such as student motivation, student acceptance of technology, and student preparedness and desire to utilize technology in learning environments. As most research samples students, a research gap involving instructors and heterogeneous groups is proposed. It is projected that quantitative research will predominate in the future, leaving qualitative and mixed approaches as areas of investigation. 

transactional distance theory TDT distance learning

1. Introduction

The term “distance learning” (DL) did not become widespread in use until the 1970s [1]. Early on, attempts were made to define it, and there were debates about what it was. One of the obstacles to distance learning was the geographic separation of learners and instructors, which was also a pedagogical concept. Moore’s proof and explanation that remote education was more concerned with pedagogy than geography [1][2] led him to establish transactional distance theory (TDT). Moore described TDT in 1973 as a discrepancy in psychological and communicative understanding that resulted from the interaction between structure and conversation. This cognitive gap might be a source of confusion between educators and their pupils [3][4]. This was an endless, relative, and ever-changing expanse; this gap or separation should have been eliminated or reduced. Though specialized, the fundamental idea was a subset of traditional teaching and learning since transactional distance existed even in formal education [5].
When it comes to DL, however, the physical separation between educators and students creates a greater sense of distance than is experienced in traditional classroom settings. Therefore, transactional distance (TD) between instructor and learner (TDT) was likely more troublesome at a distance and may have led to students’ sense of isolation, less motivation, and engagement, and, finally, attrition in early DL [2]. Moore initially proposed [1] that DL architects should consider structure and dialogue as two elements that impact TD. When discussing DL, “dialogue” referred to the back-and-forth between the educator and student, while “structure” referred to the rigidity or flexibility of the teaching techniques and procedures. Distancing yourself is determined by how much time and effort were put into the conversation. The TD increased as there was less room for dialogue and more structure.
In a course with short TD, students are guided by constant “dialogue” [6]. This might be more suitable or appealing for learners with less confidence in controlling their studies. Moore subsequently acknowledged that with limited “dialogue,” pupils were compelled to make independent judgments and generally practice “autonomy” [2]. Later, along with Kearsley, he identified three interactive components or structures [7] that must be addressed to reduce TD and offer students a meaningful learning experience. In addition to the two essential components, “structure” and “dialogue,” he introduced a third, “autonomy.” This third hypothesized component, “autonomy,” interacts with both “structure” and “conversation” to build a model or theory for comprehending DL [7].

2. TDT As a Theoretical Background for Educational Settings

Moore developed TDT, a widely used theory for designing and developing distance learning environments that has received worldwide and interdisciplinary acclaim. It creates instructional designs for distance and online learning environments [8][9][10], the framework for mobile learning MOOC settings [11], and ODL (open distance learning) [12]. TDT has been used in education for several objectives, including perceptions of excellent tutors and good tutor traits [13], anxiety performance in distance learning settings [14], optimal learning environment [15], and communication techniques between instructor and learners [16]. A list of theoretical background for educational settings based on TDT are illustrated in Table 1.
Table 1. TDT theoretical background in earlier research.
Purpose of TDT Research
Instructional designs [8][9]
Framework for mobile learning MOOC settings [10]
ODL (open distance learning) [12]
Perceptions of excellent tutors and good tutor traits [13]
Anxiety performance in DL settings [14]
Optimal learning environment [15]
Communication techniques between instructor and learners [16]

3. Theory Integration

The bulk of articles builds upon TDT with other theories. A total of 29 articles out of 42, or 69.048 percent of the total, include other theories in the SDT. The theories integrated with TDT are Bloom’s taxonomy theory, the person–environment interaction model, the theory of mediated learning experience (MLE), Computer-Based Scaffolds, the community of inquiry, rational analysis of mobile education (FRAME), self-regulated learning (SRL), the social cognitive theoretical framework, computer self-efficacy, cognitive load theory, activity theory, sociocultural theory, the social science theory, the cultural–historical theory, the activity theory, the transactional distance theory, the transactional control theory, shaping dwellings, and the stigmergy. The significant number of theories in TDT may be due to its strong explanatory power. As a consequence, TDT was combined with other theories and models to improve the explanatory capacity of such theories and models. This is not a new position or approach since it has existed in the past. The creator of TDT proposed and recommended its inclusion [17].

4. TDT Factors

Moore’s transactional distance theory (TDT) is a valuable paradigm for studying remote education [1][2][8][17][18][19][20]. TDT describes and quantifies the instructor–student learning interaction in distance education [21]. High TD between instructor and pupils may cause isolation, low motivation, and disengagement [2][20]. Moore identifies three TDT concepts: (1) structure, (2) interaction (or dialogue), and (3) learner autonomy [7].
The structure represents the interaction between the teacher, students, and technology [22][23][24]. Autonomy is the degree of structure needed; promoting interaction and fostering learner autonomy is difficult. The more structure and the less interaction, the more learner autonomy is necessary [7]. The dialogue that occurs as part of the learning process [18] assists students in conceptualizing [25]. Successful TD settings rely on the instructor delivering interaction and “appropriately” arranged learning resources. Greater, quicker, and more involved connection reduces psychological isolation [25][26]. Effective online learning requires well-structured information, the latest technology, and more interactivity [27].
Researchers categorized the article based on the TDT factors used in their studies. Seven of the forty-two evaluated studies, or 16.667 percent, employed TDT factors (structure, dialogue, and learner autonomy) without any integration [10][28][29][30][31][32][33] based on an examination of the reviewed articles. In addition, 35 of the forty-two examined articles, or 83.333 percent, incorporated other aspects into TD theory.
One study [34] has integrated self-regulated learning (SRL) with TDT in their study. Moreover, the integration between TDT and Bloom’s taxonomy theory (BTT) was the theoretical framework of [35]. In one study [36], TDT was integrated with a person–environment interaction model. One study combined the theory of mediated learning experiences (MLE) with TDT [37]. In addition, the integration between problem-based learning, computer-based scaffolds, and TDT was found in one study [32]. At the same time, one study has integrated TDT and the community of inquiry (CoI) [38]. One study integrated the rational analysis of mobile education (FRAME) with TDT [39]. Another study combined the social cognitive theoretical framework with TDT [40]. Computer self-efficacy with TDT was integrated into [14]. The integration between multiple theories, cognitive load theory, activity theory, sociocultural theory, and TDT was found in [15]. The integration of TD, social science theory, cultural–historical theory, and activity theory is discussed in [8]. Finally, transactional distance, transactional control, shaping dwellings, and stigmergy were found in [35]. For example, ref. [41] studied only two TDT factors: structure and dialogue, leaving learning autonomy alone; ref. [42] examined only dialogue from the TDT (student–content, student–interface, student–instructor, and student–student interactions).

5. Type of Samples

Understanding the kind of sample is essential for justifying the selection of samples for future research and understanding the present knowledge gap in the context of TDT research and distance learning. In light of the theory’s focus on human happiness as its endpoint, TDT studies are almost exclusively conducted with people in mind [11][35][38][43][44][45][46][47]. Based on the examination of the samples used in the selected publications, researchers can confidently say that the vast majority of samples for SDT studies of online education consist primarily of students. Thirty-five total samples were obtained, with 83.333 percent coming from students. In just 4.76 percent of the studies, lecturer samples existed (n = 2). In addition, 4.762% (n = 2) of the studies employed surveys of in-service teachers.
Moreover, both students and faculty administrators were surveyed in 2.381% (n = 1), and both students and module coordinators were surveyed in 2.381% (n = 1), as well as students and lecturers. It is possible that combining student and teacher samples is an effort to understand the motives behind the whole distance learning process from the viewpoints of both the information receiver and the instructor. In addition, under the guidance of faculty administrators, students will work closely with module coordinators to develop an in-depth comprehension of the defining features of course design concepts based on Moore’s TD theory.

6. Research Techniques and Data Analysis

In the early days of TDT’s development, quantitative research methodologies were employed to deduce the connections between TDT elements such as course structure, discourse, and student agency. Twenty-one papers, or 50% (n = 21), used quantitative research methodologies. Tactical decision-making (TDM) and the continuing online learning initiative [14][15][16][28][33][35][36][37][41][44][45][46][47][48][49][50][51] continue to use quantitative methodologies.
Quantitative methods, however, have seen a rise in favor as well. Only 19.048 percent (n = 8) of articles were published using qualitative approaches such as [52] open-ended interviews, bulletin board peers’ discussion logs, research writing assignments, video and audio transcripts and observations notes [39], focus group interviews [53], case studies [54], and content analysis [8][9][10]. The most common qualitative methods used were case studies.
In addition, 30.952% (n = 13) of the examined publications showed that mixed-method techniques were more prevalent than qualitative ones. A questionnaire and a virtual, semi-structured interview were the most commonly used mixed methods. Other methods included the following: [42] a questionnaire and focus group interview, ref. [55] questionnaire and interview, [11] artificial intelligence sentiment analysis, [12] questionnaires and in-depth interviews, ref. [13] questionnaires and semi-structured interviews, [32] questionnaire and rubric, [56] face-to-face, open-ended interviews, bulletin board discussion logs, and online assessment projects, [38] surveys, instructor journals, and learning activities, [12] a questionnaire and case design, ref. [40] SRL activities, survey answer analysis, and journal reflection, and [57] content analysis and questionnaire.

7. Geographical Locations

The study on TDT in distance learning within the setting of universities is geographically diversified. Hence, there is no particular emphasis on places. There are, nevertheless, clear indications of high scientific activity in the US. A total of 18 articles [8][10][14][15][30][32][34][37][38][39][40][44][46][49][51][52][56] (42.857%) were carried out and published in the US. There have been just four investigations undertaken in Turkey [13][42][47][55], three in Malaysia [16][35][53], two in New Zealand [31][54], two in China [36][50], and two more in India [28][58]. Eleven papers were published globally in the interim, accounting for 25.3% of the articles examined. For example, the United Kingdom (n = 1), Thailand (n = 1), Sweden (n = 1), Hong Kong (n = 1), Greece (n = 1), South Africa (n = 1), Palestine (n = 1), Malawi (n = 1), the Philippines (n = 1), and Israel (n = 1), as well as a global study (n = 1). Therefore, nothing is known about TDT in African institutions’ distance education programs.

8. Future Agenda

Based on the analysis of the evaluated articles, the most common recommendation was that, firstly, the course design or structure must be based on theories and preceding literature to integrate distance learning [34][35][36][37][38][45][46][47]. Secondly, instructors have a crucial role in distance learning contexts by providing support and encouragement. Moreover, reasonable distance education tutors and advisors create a student-centered learning environment, care about students, and have subject understanding and basic technical abilities [48][49][50][51][52]. Another recommendation was that TDT is updated to reflect the use of synchronous technologies for remote learning, especially its definition and perspective on structural aspects and how synchrony impacts learner autonomy [54]. Finally, TDT promotes and facilitates distance learning. Instructional designers learn about distance learning and how to use technology in teaching and learning [10].

References

  1. Moore, M. Towards a theory of independent learning and teaching. J. High. Educ. Policy Manag. 1973, 44, 661–679.
  2. Moore, M.G. Distance education theory. Am. J. Distance Educ. 1991, 5, 1–6.
  3. Abuhassna, H.; Awae, F.; Bayoumi, K.; Alzitawi, D.Y.; Alsharif, A.H.; Yahaya, N. Understanding Online Learning Readiness among University Students: A Bibliometric Analysis. Int. J. Interact. Mob. Technol. (Ijim) 2022, 16, 81–94.
  4. Abuhassna, H.; Busalim, A.H.; Mamman, B.; Yahaya, N.; Megat Zakaria, M.A.Z.; Al-Maatouk, Q.; Awae, F. From Student’s Experience: Does E-learning Course Structure Influenced by learner’s Prior Experience, Background Knowledge, Autonomy, and Dialogue. Contemp. Educ. Technol. 2022, 14, ep338.
  5. Lowe, W. Transactional distance theory as a foundation for developing innovative and reactive instruction. Educ. Technol. Soc. 2000, 3, 1–3.
  6. Moore, M.G.; Anderson, W.G. Handbook of Distance Education, 2nd ed.; Lawrence Erlbaum: Mahwah, NJ, USA, 2007.
  7. Moore, M.G.; Kearsley, G. Distance Education: A Systems View of Online. Learning, 3rd ed.; Library of Congress: Belmont, CA, USA, 2012.
  8. Kang, H.; Gyorke, A. Rethinking distance learning activities: A comparison of transactional distance theory and activity theory. Open Learn. J. Open Distance Learn. 2008, 23, 203–214.
  9. Dron, J. The way of the termite: A theoretically grounded approach to the design of e-learning environments. Int. J. Web Based Communities 2006, 2, 3–16.
  10. Park, Y. A Pedagogical Framework for Mobile Learning: Categorizing Educational Applications of Mobile Technologies into Four Types. Int. Rev. Res. Open Distance Learn. 2011, 12, 78–102.
  11. Hew, K.F.; Hu, X.; Qiao, C.; Tang, Y. What predicts student satisfaction with MOOCs: A gradient boosting trees supervised machine learning and sentiment analysis approach. Comput. Educ. 2019, 145, 103724.
  12. Chawinga, W.D.; Paxton, A.Z. Increasing access to higher education through open and distance learning. Empirical findings from Mzuzu University, Malawi. Int. Rev. Res. Open Distrib. Learn. 2016, 17, 1–20.
  13. Kara, M.; Can, G. Master’s Students’ Perceptions and Expectations of Good Tutors and Advisors in Distance Education. Int. Rev. Res. Open Distrib. Learn. 2019, 20, 2.
  14. Hauser, R.; Paul, R.; Bradley, J. Computer Self-Efficacy, Anxiety, and Learning in Online Versus Face to Face Medium. J. Inf. Technol. Educ. Res. 2012, 11, 141–154.
  15. Wold, K.A. Blending theories for instructional design: Creating and implementing the structure, environment, experience, and people (SEEP) model. Comput. Assist. Lang. Learn. 2011, 24, 371–382.
  16. Kayode, B.K. Effect of Communication Management on Distance Learners’ Cognitive Engagement in Malaysian Institutions of Higher Learning. Int. Rev. Res. Open Distrib. Learn. 2012, 19, 4.
  17. Jung, I. Building a theoretical framework of web-based instruction in the context of distance education. Br. J. Educ. Technol. 2001, 32, 525–534.
  18. Abuhassna, H.; Yahya, N.; Zakaria, M.A.Z.M.; Al-Maatouk, Q.; Awae, F. Guidelines for Designing Distance Learning Courses via Moodle to Enhance Students Satisfaction and Achievements. Int. J. Inf. Educ. Technol. 2021, 11, 574–582.
  19. Garrison, R. Theoretical challenges for distance education in the 21st century: A shift from structural to transactional issues. Int. Rev. Res. Open Distrib. Learn. 2000, 1, 1–17.
  20. Delgaty, L. Transactional distance theory: A critical view of the theoretical and pedagogical underpinnings of e-learning. In Interactive Multimedia—Multimedia Production and Digital Storytelling; IntechOpen: London, UK, 2018.
  21. Tirado-Morueta, R.; Sánchez-Herrera, R.; Márquez-Sánchez, M.A.; Mejías-Borrero, A.; Andujar-Márquez, J.M. Exploratory study of the acceptance of two individual practical classes with remote labs. Eur. J. Eng. Educ. 2018, 43, 278–295.
  22. Moore, M. Three types of interaction. Am. J. Distance Educ. 1989, 3, 1–7.
  23. Lowe, P.; Murray, S.; Li, D.; Lindsay, E. Remotely Accessible Laboratories—Enhancing Learning Outcomes; Australian Learning and Teaching Council: Sydney, Australia, 2008.
  24. Sher, A. Assessing the relationship of student-instructor and student-student interaction to student learning and satisfaction in web-based online learning environment. J. Interact. Online Learn. 2009, 8, 2.
  25. Wei, J.; Treagust, D.F.; Mocerino, M.; Lucey, A.D.; Zadnik, M.G.; Lindsay, E.D. Understanding interactions in faceto- face and remote undergraduate science laboratories: A literature review. Discip. Interdiscip. Sci. Educ. Res. 2019, 1, 14.
  26. Moore, M.; William, A. Handbook of Distance Education; Routledge: Oxfordshire, UK, 2007.
  27. Sun, A.; Chen, X. Online education and its effective practice: A research review. J. Inf. Technol. Educ. 2016, 15, 157–190.
  28. Achuthan, K.; Raghavan, D.; Shankar, B.; Francis, S.P.; Kolil, V.K. Impact of remote experimentation, interactivity and platform effectiveness on laboratory learning outcomes. Int. J. Educ. Technol. High. Educ. 2021, 18, 38.
  29. Alhazbi, S.; Hasan, M.A. The Role of Self-Regulation in Remote Emergency Learning: Comparing Synchronous and Asynchronous Online Learning. Sustainability 2021, 13, 11070.
  30. Loose, C.C.; Ryan, M.G. Cultivating Teachers When the School Doors Are Shut: Two Teacher-Educators Reflect on Supervision, Instruction, Change and Opportunity During the Covid-19 Pandemic. Front. Educ. 2020, 5, 582561.
  31. Limtrairut, P.; Marshall, S. A New Design Guideline for Mobile Learning Application: Transactional Distance Perspective. In Proceedings of the 2020 IEEE 9th Global Conference on Consumer Electronics (GCCE), Kobe, Japan, 13–16 October 2020; pp. 610–614.
  32. Weiss, D.M.; Belland, B.R. PBL Group Autonomy in a High School Environmental Science Class. Technol. Knowl. Learn. 2018, 23, 83–107.
  33. Huang, X.; Chandra, A.; DePaolo, C.A.; Simmons, L.L. Transactional distance in web-based learning environments. Br. J. Educ. Technol. 2016, 47, 734–747.
  34. Andrade, M.S. Dialogue and Structure: Enabling Learner Self-Regulation in Technology-Enhanced Learning Environments. Eur. Educ. Res. J. 2014, 13, 563–574.
  35. Abuhassna, H.; Al-Rahmi, W.M.; Yahya, N.; Zakaria MA, Z.M.; Kosnin, A.B.; Darwish, M. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. Int. J. Educ. Technol. High. Educ. 2020, 17, 38.
  36. Yu, J.; Huang, C.; Han, Z.; He, T.; Li, M. Investigating the Influence of Interaction on Learning Persistence in Online Settings: Moderation or Mediation of Academic Emotions? Int. J. Environ. Res. Public Health 2020, 17, 2320.
  37. Elyakim, N.; Reychav, I.; Offir, B.; McHaney, R. Perceptions of Transactional Distance in Blended Learning Using Location-Based Mobile Devices. J. Educ. Comput. Res. 2019, 57, 131–169.
  38. Jennifer Shea, M.; Ernita, J.; Janey, Q.W. Pedagogical Design Factors That Enhance Learning in Hybrid Courses: A Contribution to Design Based Instructional Theory. J. Public Aff. Educ. 2016, 22, 381–397.
  39. Levene, J.; Seabury, H. Evaluation of Mobile Learning: Current Research and Implications for Instructional Designers. TechTrends 2015, 59, 46–52.
  40. Jowallah, R. An investigation into the management of online teaching and learning spaces: A case study involving graduate research students. Int. Rev. Res. Open Distrib. Learn. 2014, 15, 186–198.
  41. Rimpelä, A.; Lindfors, P.; Kinnunen, J.M.; Myöhänen, A.; Hotulainen, R.; Koivuhovi, S.; Vainikainen, M.-P. The Way of Distance Teaching Is Related to Adolescent Students’ Health and Loneliness during the School Closure in Finland. Int. J. Environ. Res. Public Health 2021, 18, 12377.
  42. Kayaduman, H. Student interactions in a flipped classroom-based undergraduate engineering statistics course. Comput. Appl. Eng. Educ. 2021, 29, 969–978.
  43. Abuhassna, H.; Yahaya, N. Students’ Utilization of Distance Learning through an Interventional Online Module Based on Moore Transactional Distance Theory. Eurasia J. Math. Sci. Technol. Educ. 2018, 14, 3043–3052.
  44. Swart, W.; MacLeod, K. Evaluating Learning Space Designs for Flipped and Collaborative Learning: A Transactional Distance Approach. Educ. Sci. 2021, 11, 292.
  45. Gavrılıs, V.; Mavroıdıs, I.; Gıossos, Y. Transactional distance and student satisfaction in a postgraduate distance learning program. Turk. Online J. Distance Educ. 2020, 21, 48–62.
  46. Stein, D.S.; Wanstreet, C.E.; Calvin, J.; Overtoom, C.; Wheaton, J.E. Bridging the Transactional Distance Gap in Online Learning Environments. Am. J. Distance Educ. 2005, 19, 105–118.
  47. Horzum, M.B. Interaction, Structure, Social Presence, and Satisfaction in Online Learning. Eurasia J. Math. Sci. Technol. Educ. 2015, 11, 505–512.
  48. Marcum, J.; Kim, Y. Oral Language Proficiency in Distance English-Language Learning. CALICO J. 2020, 37, 148–168.
  49. Huang, X.; Chandra, A.; DePaolo, C.; Cribbs, J.; Simmons, L. Measuring transactional distance in web-based learning environments: An initial instrument development. Open Learn. J. Open Distance e-Learn. 2015, 30, 106–126.
  50. Goel, L.; Zhang, P.; Templeton, M. Transactional distance revisited: Bridging face and empirical validity. Comput. Hum. Behav. 2012, 28, 1122–1129.
  51. Chen, Y.-J. Dimensions of transactional distance in the world wide web learning environment: A factor analysis. Br. J. Educ. Technol. 2001, 32, 459–470.
  52. Sato, T.; Haegele, J.A. Physical Education Preservice Teachers’ Academic and Social Engagement in Online Kinesiology Course. J. Digit. Learn. Teach. Educ. 2019, 35, 181–196.
  53. Ustati, R.; Hassan, S.S.S. Distance learning students’ need: Evaluating Interactions from Moore’s Theory of Transactional Distance. Turk. Online J. Distance Educ. 2013, 14, 1302–6488.
  54. Falloon, G. Making the Connection. J. Res. Technol. Educ. 2011, 43, 187–209.
  55. Kara, M. Transactional distance and learner outcomes in an online EFL context, Open Learning: The Journal of Open. Distance e-Learn. 2021, 36, 45–60.
  56. Sato, T.; Haegele, J.A. Undergraduate kinesiology students’ experiences in online motor development courses. Online Learn. 2018, 22, 271–288.
  57. Offir, B.; Lev, Y.; Lev, Y.; Barth, I.; Shteinbok, A. an integrated analysis of verbal and nonverbal interaction in conventional and distance learning environments. J. Educ. Comput. Res. 2004, 31, 101–118.
  58. Mitra, B.; Kode, S.; Cheruvu, A. Mobile Learning: A Pedagogical Framework to Facilitate Distributed Learning through Collaborative Approach for Enhancing English Language Communication Skills. In Proceedings of the 2015 IEEE Seventh International Conference on Technology for Education (T4E), Warangal, India, 10–12 December 2015; pp. 91–94.
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