Relevant bibliographies by topics / Online learning interaction

Academic literature on the topic 'Online learning interaction'

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Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Online learning interaction"

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Gusić, Laura. "Learning language online – interaction without interaction." Educational Role of Language Journal 2021-1, no. 5 (September 27, 2021): 30–31. http://dx.doi.org/10.36534/erlj.2021.01.03.

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Zayabalaradjane, Zayapragassarazan, and Thomas V. Chacko. "Interaction Analysis of FAIMER Mentor-Learner Web Online Collaborative Learning Session." Malaysian Online Journal of Educational Technology 8, no. 2 (April 1, 2020): 12–27. http://dx.doi.org/10.17220/mojet.2020.02.002.

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Mayende, Godfrey, Andreas Prinz, Ghislain Maurice Norbert Isabwe, and Paul Birevu Muyinda. "Learning Groups in MOOCs: Lessons for Online Learning in Higher Education." International Journal of Engineering Pedagogy (iJEP) 7, no. 2 (May 19, 2017): 109. http://dx.doi.org/10.3991/ijep.v7i2.6925.

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when there is interaction within online learning groups, meaningful learning is achieved. Motivating and sustaining effective student interactions requires planning, coordination and implementation of curriculum, pedagogy and technology. For our aim to understand online learning group processes to identify effective online learning group mechanisms, comparative analysis was used on a massive open online course (MOOC) run in 2015 and 2016. Qualitative (interaction on the platform) and quantitative (survey) methods were used. The findings revealed several possible ways to improve online learning group processes. This paper concludes that course organization helped in increasing individual participation in the groups. Motivation by peers helped to increase sustainability of interaction in the learning groups. Applying these mechanisms in higher education can make online learning groups more effective.
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Hung, Min-Ling. "Online Learning Readiness." International Journal of Technology and Human Interaction 12, no. 1 (January 2016): 51–62. http://dx.doi.org/10.4018/ijthi.2016010104.

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The purpose of this study was to examine whether online students' course-related readiness would undergo changes between midterm exams and final exams, and which student-readiness factors might predict students' willingness to take an online course again. The analysis used survey data from 217 students enrolled in an online course that was presented three times over three consecutive semesters. The results of this study were as follows: (1) an increase in communication self-efficacy and a decrease in learner control and in motivation for learning from middle of the semester to the end of the semester; (2) communication self-efficacy and learning motivation were statistically significant predictors of the students' willingness to take future online courses.
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Keengwe, Jared, and Gary Schnellert. "Fostering Interaction to Enhance Learning in Online Learning Environments." International Journal of Information and Communication Technology Education 8, no. 3 (July 2012): 28–35. http://dx.doi.org/10.4018/jicte.2012070104.

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Interaction is central to educational experiences in online learning environments. Interaction enhances learning by fostering three types of learning interaction: learner-instructor, learner-learner, and learner-content. Additionally, online students generally perceive interaction as an effective means of learning. Therefore, it is important for online instructors to create opportunities for interaction in online learning environments. This article stimulates reflections on the critical role of interaction in online learning. A primary implication based on the challenges reported in the study is that instructors need to reflect on effective ways to design and implement successful online learning environments. For instance, instructors could anticipate these challenges by focusing on the development of online tools such as wikis to enhance effective online interaction.
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Alkhalaf, Salem, Jeremy Nguyen, Anne Nguyen, and Steve Drew. "Online Learner Satisfaction and Collaborative Learning." International Journal of Information and Communication Technology Education 9, no. 2 (April 2013): 66–78. http://dx.doi.org/10.4018/jicte.2013040106.

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Despite the considerable potential for e-learning to improve learning outcomes, particularly for female students and students who need to rely on distance learning, feedback from current users of e-learning systems in the Kingdom of Saudi Arabia (KSA) suggests a relatively low level of satisfaction. This study adopts a mixed-methods approach in investigating the underlying reasons for this situation. Results indicate that students are not unhappy with the information technology infrastructure or with other technical aspects. However, many students report a low level of interaction between them and their fellow students. When interactions do occur, students indicate overwhelmingly that they find such interactions useful. Together, these findings suggest that greater student interaction, which could be fostered through the use of collaborative learning, is likely to be both popular with students and beneficial to their learning outcomes.
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Shalev-Shwartz, Shai. "Online Learning and Online Convex Optimization." Foundations and Trends® in Machine Learning 4, no. 2 (2011): 107–94. http://dx.doi.org/10.1561/2200000018.

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Pestean, V. "Didactic Approaches for a Quality Digital Learning." 12th GLOBAL CONFERENCE ON BUSINESS AND SOCIAL SCIENCES 12, no. 1 (October 8, 2021): 31. http://dx.doi.org/10.35609/gcbssproceeding.2021.12(31).

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The research whose result is illustrated in this paper investigates the ways in which teachers overcome the temptation to limit the lesson only to content delivery and assessment. This paper identifies and explains the types of interactions facilitated by teachers during online classes. An important part of the paper is intended to clasify the most successful techniques that stimulate synchronous and asynchronous interactions in teacher-student relationship in primary school. The professional experience of the teachers included in this research is explored and capitalized on, in order to decide on the right ways for connection, as well as productive interaction in the online environment. Keywords: Interactions Facilitated, Online Classes, Connection And Productive Interaction In The Online Environment
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O'Rourke, Breffni. "Form-focused Interaction in Online Tandem Learning." CALICO Journal 22, no. 3 (January 14, 2013): 433–66. http://dx.doi.org/10.1558/cj.v22i3.433-466.

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Youngmeyer, David. "A crash course in online learning." Interactions 27, no. 4 (July 9, 2020): 8–9. http://dx.doi.org/10.1145/3406106.

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Dissertations / Theses on the topic "Online learning interaction"

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Wang, Dawei. "Enhancing individualised learning and interaction in online learning environments." Thesis, Robert Gordon University, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.491201.

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The quality of the student learning experience in an online learning course has raised many debates in educational studies. Evidence found in current literature indicates that individualised learning and interactive learning do contribute to the student learning experience in online learning courses. However, there is little evidence of any major studies that have tried to explore the impact of both individualised learning and interactive learning on the students' experience.
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Farahani, Gohar Omidvar. "Existence and Importance of Online Interaction." Diss., Virginia Tech, 2003. http://hdl.handle.net/10919/27169.

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This research explored the existence and importance of interaction in online courses as perceived by online learners and instructors. The study was based on data from online students and instructors in the fall 2002 semester at Mid-Atlantic Community College(1). Two web-based surveys were used to collect data. Eighty-eight of 267 online students completed the survey, for a response rate of 33%. The study was based on constructivist theory which suggested that students learn by actively participating in the learning process through interaction with the instructor, other students, and course materials. This interaction was measured by different online interaction modalities and a five-step interactivity model developed by Salmon. This model suggested that the intensity of interactivity involves five steps: access and motivation, online socialization, information exchange, knowledge construction, and development. In addition, student characteristics (age and gender) and pedagogical variables (online experience and learning preferences) were included. Findings of the survey revealed that students perceived a moderate to high level of availability in a majority of the interactivity modalities. The highest interaction was reported between students and instructor through email communication and feedback on students? work by instructors. In addition, student ratings of the availability of different interaction modalities in online instruction were correlated with their perceptions of the importance of these modalities. Students reported satisfaction with the level of interactivity in their online courses. In contrast, responses to Salmon?s model revealed a high level of unavailability of the various interactivity criteria. The result of instructor survey, based on 13 responses, revealed that online instructors perceive interaction with students through email communication and providing feedback on their work were important. They did not perceive many of the interactivity criteria introduced by this research to be important. Therefore, they reported these criteria as unavailable in their online courses. This study is important because the extent of systematic research on availability and importance of online interaction is limited. (1)- To preserve the anonymity of respondents, this name is a pseudonym.
Ph. D.
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Barrett, Knight Rachel Michele. "An examination of interaction in online language learning classrooms." Thesis, Indiana State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3717303.

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The purpose of this mixed-method study was to examine the three different types of interactions (student-student, student-instructor, and student-content) that occur in an online language learning classroom and whether the amount of time spent predicted the performance in two key areas, grammar proficiency and vocabulary knowledge. This study also examined the way students felt about motivating themselves in an online language learning classroom. The study involved 40 students enrolled in an entirely online entry-level Spanish course who completed a survey about their experience in the online environment and an assessment of their knowledge of vocabulary and grammar points in the language.

The multiple regression analyses revealed that the amount of time spent in interactions with the content for the course was a predictor for grammar proficiency, but no other interactions were predictors for grammar proficiency or vocabulary knowledge. The participants discussed the difficulty of motivating themselves and how they stayed motivated in the online environment. This study provided some considerations for practitioners in an online environment, but also called into question the efficacy of learning a language in an entirely online environment.

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Miller, Jane Rae. "Gender, language and interaction styles in online learning environments." Thesis, Glasgow Caledonian University, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.404667.

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Albloushi, Shaima Abdullah. "Online Collaborative Learning and Interaction Among Pre-Service Teachers." Ohio University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1553088752167938.

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Borup, Jered A. "Types, Subjects, and Purposes of K-12 Online Learning Interaction." BYU ScholarsArchive, 2013. https://scholarsarchive.byu.edu/etd/3711.

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Although K-12 online learning has experienced exceptional growth, research in the area has lagged behind. This dissertation addressed this gap in the literature using a multiple article dissertation format. The first article used survey data from two online English courses at the Open High School of Utah (OHSU) to examine students' reported interactions with content, peers, and instructors. The large majority of students viewed all investigated types of interaction as educational and motivational. Students perceived learner--instructor and learner--content interactions to have significantly higher educational value than learner--learner interactions, and viewed learner--instructor interaction to be significantly more motivational than learner--content interaction. Furthermore, nine significant correlations were found between the time students spent on human interaction and course outcomes. The second article examined learner-parent and parent-instructor interactions within the same context. Similar to the first article, survey data was used to measure parents' and students' perceived quantity and quality of parental interactions with students and teachers. It was found that generally students and parents viewed parent--instructor and learner--parent interactions as motivational. Students viewed learner--parent interaction as significantly more motivational than did their parents. The quantity of reported parental interactions tended to negatively correlate with course outcomes. These negative correlations may be the result of parents' tendency to increase interaction levels following poor student performance and may not reflect the actual impact of parental interactions on individual student learning. When discussing the results in the second article, the claim was made that future research should look beyond the quantity of interactions and develop a theoretical framework that identifies and categorizes the roles of individuals in improving student outcomes. The third article of this dissertation presents such a framework that can help guide K-12 online research and design. The Adolescent Community of Engagement (ACE) framework consists of four main constructs that make up a K-12 online learning community. The first three (student engagement, teacher engagement, and peer engagement) build on previously established online frameworks that originally emerged from higher education contexts. In addition, the ACE framework recognizes the role of parents in their child's learning and introduces a fourth construct, parent engagement, which builds on two previously established face-to-face frameworks.
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Van, Ryneveld Linda. "Surviving the game interaction in an adult online learning community /." Pretoria : [s.n.], 2004. http://upetd.up.ac.za/thesis/available/etd-03082005-220804/.

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Jain, Pawan. "Building learning communities facilitating interaction in computer mediated online courses /." Laramie, Wyo. : University of Wyoming, 2008. http://proquest.umi.com/pqdweb?did=1799869821&sid=1&Fmt=2&clientId=18949&RQT=309&VName=PQD.

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Sousa, Sonia. "Online distance learning : exploring the interaction between trust and performance." Thesis, Sheffield Hallam University, 2006. http://shura.shu.ac.uk/20386/.

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The research reported in this thesis makes a contribution towards a comprehensive understanding of the online distance learning process. This research specifically addresses the effects of learners' trust in relation to particular trust factors in their academic performance when studying at a distance. An initial theoretical framework is outlined, which encompasses investigated areas such as distance education, online distance learning, trust, performance, as-sessment, online communities, co-operation and collaboration. Subsequently, a two stage empirical research process is described. Firstly, a survey is outlined to enable the construction of a set of trust factors characterising learners' beliefs in relation to online distance learning issues as well as to provide the student group profile. Secondly, a study which accounts for both a quantitative and qualitative data collec-tion is presented. Quantitative data was used to test an hypothesis which explored the relation between trust and performance under different conditions. Qualitative data was gathered to better understand and explain the results from the first stage. Results identified the importance of fostering trust in a distance learning community and in providing trustful online distance learning environments and reflects on the understanding of the concept of trust and possible implications for the online distance learning design and it pedagogy. Implications for online distance learners' needs and support are also identified. Results also confirmed the interaction between trust and performance although some results were unexpected. For instance, under certain conditions a negative correlation between trust and academic performance was identified. These results might, somehow, be related to Cape Verde's specific characteristics such as limited broadband access to the Internet and access to ICT facilities, serious water and electricity shortages, limited agricultural possibilities and shortage of higher education opportunities. Within this context, this work's major contributions are at the intersection of areas such as trust, online distance learning and learners' academic performance. Which offers an. increased understanding of the effects of learners' trust beliefs over their academic performance in online distance learning processes. An additional contribution was the development of an Information and Communication Technologies Online distance learning module for every initial year of the Universidade Jean Piaget de Cabo Verde undergraduate programme, thus contributing to reducing the existing gap between developed and developing countries. A complementary contribution is the identification of a number of consequent research questions which pave the way for future research work.
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Willman, Martin. "Machine Learning to identify cheaters in online games." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-170973.

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Cheating in online games is a problem both on the esport stage and in the gaming community. When a player cheats, the competitors do not compete on the same terms anymore and this becomes a major problem when high price pools are involved in online games. In this master thesis, a machine learning approach will be developed and tested to try to identify cheaters in the first-person shooter game Counter-Strike : Global Offensive. The thesis will also go through how the game Counter-Strike : Global Offensive works, give examples of anti-cheat softwares that exists, analyse different cheats in the game, consider social aspects of cheating in online games, and give an introduction to machine learning. The machine learning approach was done by creating and evaluating a recurrent neural network with data from games played with the cheat aimbot and without the cheat aimbot. The recurrent neural network that was created in this master thesis should be considered as the first step towards creating a reliable anti-cheat machine learning algorithm. To possible increase the result of the recurrent neural network, more data and more data points from the game would be needed.
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Books on the topic "Online learning interaction"

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Student-teacher interaction in online learning environments. Hershey, PA: Information Science Reference, 2015.

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EPedagogy in online learning: New developments in web mediated human computer interaction. Hershey, PA: Information Science Reference, 2013.

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Pasfield-Neofitou, Sarah E. Online communication in a second language: Social interaction, language use, and learning Japanese. Bristol: Multilingual Matters, 2012.

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Peñalvo, Francisco José García, and Antonio M. Seoane Pardo. Online tutor 2.0: Methodologies and case studies for successful learning. Hershey, PA: Information Science Reference, an imprint of IGI Global, 2014.

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1961-, Houghton Timothy J., and Pratt Michele M, eds. Online learning: The easy way! [Flint, Mi.]: Baker College, 2008.

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Transforming education with new media: Participatory pedagogy, interactive learning and Web 2.0. New York: PETER LANG, 2012.

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Watkins, Ryan. E-learning companion: A student's guide to online success. 3rd ed. Boston, Mass: Wadsworth Cengage Learning, 2011.

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Watkins, Ryan. E-learning companion: A student's guide to online success. 3rd ed. Boston, Mass: Wadsworth Cengage Learning, 2011.

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Watkins, Ryan. E-learning companion: A student's guide to online success. Boston, MA: Houghton Mifflin Co., 2005.

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Watkins, Ryan. E-learning companion: A student's guide to online success. 2nd ed. Boston: Houghton Mifflin Co., 2008.

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Book chapters on the topic "Online learning interaction"

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Kavanaugh, Andrea, ThanThan Zin, Joseph Schmitz, Mary Beth Rosson, B. Joon Kim, and John M. Carroll. "Local Groups Online: Political Learning and Participation." In Human-Computer Interaction Series, 55–73. London: Springer London, 2009. http://dx.doi.org/10.1007/978-1-84800-332-3_11.

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Dekel, Ofer, Joseph Keshet, and Yoram Singer. "An Online Algorithm for Hierarchical Phoneme Classification." In Machine Learning for Multimodal Interaction, 146–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30568-2_13.

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Ke, Fengfeng, and Alicia Fedelina Chávez. "Diversity in Online Learning Interaction and Participation." In Web-Based Teaching and Learning across Culture and Age, 67–92. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-0863-5_4.

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Berlanga, Adriana, Ellen Rusman, Marlies Bitter-Rijpkema, and Peter Sloep. "Guidelines to Foster Interaction in Online Communities." In Learning Network Services for Professional Development, 27–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00978-5_3.

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Tu, Chih-Hsiung, Cherng-Jyh Yen, Emrah Emre Ozkeskin, and Laura E. Sujo-Montes. "From Online Interaction to Social Learning Analytics and Community Building." In Learning Technologies and User Interaction, 134–57. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003089704-10.

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Li, Yun, and Sherman Finch. "Using Sound to Enhance Interactions in an Online Learning Environment." In Learning Technologies and User Interaction, 118–33. New York: Routledge, 2021. http://dx.doi.org/10.4324/9781003089704-9.

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Kidd, Shay. "Tools for Communication and Interaction in Online Mathematics Teaching and Learning." In Teaching and Learning Mathematics Online, 163–88. First edition. | Boca Raton : C&H/CRC Press, 2020.: Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9781351245586-11.

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Dogan, Gulustan, Seydi Alkan, Alper Bayazit, and Gözde M. Demirci. "A Learning Analytics Case Study: Relation of Students Learning Approach to Online Learning Environment Behaviours." In Digital Interaction and Machine Intelligence, 135–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74728-2_13.

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Ovaska, Saila. "User Experience and Learning Experience in Online HCI Courses." In Human-Computer Interaction – INTERACT 2013, 447–54. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40498-6_34.

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Zain, Farah M., Saidatul M. Sahimi, Effariza Hanafi, Ahmad Hanizar A. Halim, and Abd Karim Alias. "A Study of Students’ Interaction in Edmodo Social Learning Platform." In Envisioning the Future of Online Learning, 147–58. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0954-9_13.

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Conference papers on the topic "Online learning interaction"

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CHOW, LINDA, and REX SHARMAN. "ONLINE INTERACTION: TRYING TO GET IT RIGHT." In Enhancing Learning Through Technology. WORLD SCIENTIFIC, 2006. http://dx.doi.org/10.1142/9789812772725_0008.

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Raducanu, Bogdan, and Jordi Vitria. "Online Learning for Human-Robot Interaction." In 2007 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2007. http://dx.doi.org/10.1109/cvpr.2007.383438.

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C. Banna, Jinan, Maria Stewart, Grace Lin, and Marie K. Fialkowski. "Fostering Interaction in an Online Introductory Nutrition Course." In Annual International Conference on Education & e-Learning. Global Science & Technology Forum (GSTF), 2014. http://dx.doi.org/10.5176/2251-1814_eel14.15.

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Kim, Sun Hee, and Hikyoung Lee. "LANGUAGE LEARNING OPPORTUNITIES IN ONLINE INTERCULTURAL PEER INTERACTION." In International Conference on Education and New Learning Technologies. IATED, 2017. http://dx.doi.org/10.21125/edulearn.2017.0891.

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Wei, Huan, Haotian Li, Meng Xia, Yong Wang, and Huamin Qu. "Predicting student performance in interactive online question pools using mouse interaction features." In LAK '20: 10th International Conference on Learning Analytics and Knowledge. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3375462.3375521.

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Ogunyemi, Abiodun, Merja Bauters, Jana Pejoska, and James Sunney Quaicoe. "Designing for Active Engagement in Online Learning Environments." In AfriCHI 2021: 3rd African Human-Computer Interaction Conference. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448696.3448744.

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Wang, Yonggu, and Xiaojuan Li. "Social Network Analysis of Interaction in Online Learning Communities." In Seventh IEEE International Conference on Advanced Learning Technologies (ICALT 2007). IEEE, 2007. http://dx.doi.org/10.1109/icalt.2007.228.

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Gouko, Manabu, Yuichi Kobayashi, and Chyon Hae Kim. "Online learning of exploratory behavior through human-robot interaction." In HRI'14: ACM/IEEE International Conference on Human-Robot Interaction. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2559636.2563686.

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Dehghan, Masood, Zichen Zhang, Mennatullah Siam, Jun Jin, Laura Petrich, and Martin Jagersand. "Online Object and Task Learning via Human Robot Interaction." In 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. http://dx.doi.org/10.1109/icra.2019.8794036.

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Tasnim Wan Hussin, Wan Nur, Jamalludin Harun, and Nurbiha A. Shukor. "Online Tools for Collaborative Learning to Enhance Students Interaction." In 2019 7th International Conference on Information and Communication Technology (ICoICT). IEEE, 2019. http://dx.doi.org/10.1109/icoict.2019.8835197.

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Reports on the topic "Online learning interaction"

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Chingos, Matthew, Christine Mulhern, Rebecca Griffiths, and Richard Spies. Interactive Online Learning on Campus. New York: Ithaka S+R, August 2015. http://dx.doi.org/10.18665/sr.22522.

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Bowen, William, Kelly Lack, Matthew Chingos, and Thomas Nygren. Interactive Learning Online at Public Universities. New York: Ithaka S+R, August 2015. http://dx.doi.org/10.18665/sr.22464.

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Im, Hyunjoo, and Young Ha. Effect of Customizable Interactive Online Learning Tools on Students� Engagement in the Learning Process: Flow and Utilitarian/Hedonic Values. Ames (Iowa): Iowa State University. Library, January 2019. http://dx.doi.org/10.31274/itaa.8243.

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Baloch, Imdad, Tom Kaye, Saalim Koomar, and Chris McBurnie. Pakistan Topic Brief: Providing Distance Learning to Hard-to-reach Children. EdTech Hub, June 2020. http://dx.doi.org/10.53832/edtechhub.0026.

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The COVID-19 pandemic has resulted in mass school closures across the world. It is expected that the closures in low- and -middle-income countries (LMICs) will have long-term negative consequences on education and also on broader development outcomes. Countries face a number of obstacles to effectively delivering alternative forms of education. Obstacles include limited experience in facing such challenges, limited teacher digital and pedagogical capacity, and infrastructure constraints related to power and connectivity. Furthermore, inequalities in learning outcomes are expected to widen within LMICs due to the challenges of implementing alternative modes of education in remote, rural or marginalised communities. It is expected that the most marginalised children will feel the most substantial negative impacts on their learning outcomes. Educational technology (EdTech) has been identified as a possible solution to address the acute impact of school closures through its potential to provide distance education. In this light, the DFID Pakistan team requested the EdTech Hub develop a topic brief exploring the use of EdTech to support distance learning in Pakistan. Specifically, the team requested the brief explore ways to provide distance education to children in remote rural areas and urban slums. The DFID team also requested that the EdTech Hub explore the different needs of those who have previously been to school in comparison to those who have never enrolled, with reference to EdTech solutions. In order to address these questions, this brief begins with an overview of the Pakistan education landscape. The second section of the brief explores how four modes of alternative education — TV, interactive radio instruction, mobile phones and online learning — can be used to provide alternative education to marginalised groups in Pakistan. Multimodal distance-learning approaches offer the best means of providing education to heterogeneous, hard-to-reach groups. Identifying various tools that can be deployed to meet the needs of specific population segments is an important part of developing a robust distance-learning approach. With this in mind, this section highlights examples of tools that could be used in Pakistan to support a multimodal approach that reaches the most hard-to-reach learners. The third and final section synthesises the article’s findings, presenting recommendations to inform Pakistan’s COVID-19 education response.<br> <br> This topic brief is available on Google Docs.
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You might also be interested in the extended bibliographies on the topic 'Online learning interaction' for particular source types:
Journal articles Dissertations / Theses Books
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