Abstract
Tools for learning analytics are becoming essential features of Learning Management Systems (LMS) and various course delivery platforms. These tools collect data from online learning platforms, analyze the collected data, and present the extracted information in a visually appealing manner. Representing the design-level concerns of such tools is one of the significant challenges faced by software developers. One way of overcoming this challenge is to adopt architectural perspectives which is a mechanism used by software architects to capture high-level design concerns. In this Chapter, we present an architectural perspective of such learning analytics tools and components. The primary objective of the chapter is to describe the functional elements and non-functional properties supported by such tools. Further, the chapter describes various techniques for realizing these functional and non-functional elements. Such an architectural perspective is useful in two different ways. First, the design knowledge represented through an architectural perspective is potentially useful to communicate the design and implementation of a learning analytics based system. Second, the architectural perspectives can also be used to evaluate the design of the tools in achieving their stated goals.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aderaldo, C.M., Mendonça, N.C., Pahl, C., Jamshidi, P.: Benchmark requirements for microservices architecture research. In: Proceedings of the 1st International Workshop on Establishing the Community-Wide Infrastructure for Architecture-Based Software Engineering, ECASE ’17, pp. 8–13, Piscataway, NJ, USA. IEEE Press (2017)
Adjei, S.A., Botelho, A.F., Heffernan, N.T.: Predicting student performance on post-requisite skills using prerequisite skill data: an alternative method for refining prerequisite skill structures. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 469–473. ACM (2016)
Adjei, S.A., Botelho, A.F., Heffernan, N.T.: Sequencing content in an adaptive testing system: the role of choice. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 178–182. ACM (2017)
Aghababyan, A., Lewkow, N., Baker, R.: Exploring the asymmetry of metacognition. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 115–119. ACM (2017)
Agnihotri, L., Essa, A., Baker, R.S.: Impact of student choice of content adoption delay on course outcomes. In: LAK, pp. 16–20 (2017)
Allen, L.K., Mills, C., Jacovina, M.E., Crossley, S., D’mello, S., McNamara, D.S.: Investigating boredom and engagement during writing using multiple sources of information: the essay, the writer, and keystrokes. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 114–123. ACM (2016)
Allen, L.K., Perret, C.A., Likens, A.D., McNamara, D.S.: What’d you say again?: recurrence quantification analysis as a method for analyzing the dynamics of discourse in a reading strategy tutor. In: LAK, pp. 373–382 (2017)
Andrade, A.: Understanding student learning trajectories using multimodal learning analytics within an embodied-interaction learning environment. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK ’17, pp. 70–79, New York, NY, USA. ACM (2017)
Avila, C., Baldiris, S., Fabregat, R., Graf, S.: ATCE: an analytics tool to trace the creation and evaluation of inclusive and accessible open educational resources. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 183–187. ACM (2017)
Bachmann, F., Bass, L., Klein, M.: Deriving architectural tactics: a step toward methodical architectural design. Technical report, Carnegie-Mellon University, Pittsburgh, PA, Software Engineering Institute (2003)
Barnes, T.: The q-matrix method: mining student response data for knowledge
Bøegh, J.: A new standard for quality requirements. IEEE Softw. 2, 57–63 (2008)
Bos, N., Brand-Gruwel, S.: Student differences in regulation strategies and their use of learning resources: implications for educational design. In: Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, pp. 344–353. ACM (2016)
Brunskill, E.: Estimating prerequisite structure from noisy data. In: EDM, pp. 217–222. Citeseer (2011)
Burns, S., Corwin, K.: Automating student survey reports in online education for faculty and instructional designers. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 590–591. ACM (2017)
Buschmann, F., Meunier, R., Rohnert, H., Sommerlad, P., Stal, M.: Pattern-Oriented Software Architecture, Volume 1: A System of Patterns (1996)
Chen, L., Dubrawski, A.: Learning from learning curves: discovering interpretable learning trajectories. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 544–545. ACM (2017)
Chiu, M.M., Chow, B.W.-Y., Joh, S.W.: How to assign students into sections to raise learning. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, pp. 95–104. ACM (2017)
Corrin, L., de Barba, P.G., Bakharia, A.: Using learning analytics to explore help-seeking learner profiles in MOOCs. In: Proceedings of the Seventh International Learning Analytics & Knowledge Conference, LAK ’17, pp. 424–428, New York, NY, USA. ACM (2017)
Desmarais, M.C., Maluf, A., Liu, J.: User-expertise modeling with empirically derived probabilistic implication networks. User Model. User Adapt. Interact. 5(3-4), 283–315 (1995)
Esaki, K., Azuma, M., Komiyama, T.: Introduction of quality requirement and evaluation based on ISO/IEC square series of standard. In: International Conference on Trustworthy Computing and Services, pp. 94–101. Springer (2012)
Ferguson, R.: Learning analytics: drivers, developments and challenges. Int. J. Technol. Enhanc. Learn. 4(5–6), 304–317 (2012)
Gorton, I.: Software quality attributes. In: Essential Software Architecture, pp. 23–38. Springer (2011)
Harlen, W., James, M.: Assessment and learning: differences and relationships between formative and summative assessment. Assess. Educ. Princ. Policy Pract. 4(3), 365–379 (1997)
Jayaprakash, S.M., Moody, E.W., LaurÃa, E.J.M., Regan, J.R., Baron, J.D.: Early alert of academically at-risk students: an open source analytics initiative. J. Learn. Anal. 1(1), 6–47 (2014)
Nabeel, M., Shang, N., Bertino, E.: Efficient privacy preserving content based publish subscribe systems. In: Proceedings of the 17th ACM Symposium on Access Control Models and Technologies, pp. 133–144. ACM (2012)
Paliwal, G., Kiwelekar, A.W.: A product line architecture for mobile patient monitoring system. In: Mobile Health, pp. 489–511. Springer (2015)
Rozanski, N., Woods, E.: Software Systems Architecture: Working With Stakeholders Using Viewpoints and Perspectives, 2nd edn. Addison-Wesley Professional (2011)
Sreenivasa Sarma, B.H., Ravindran, B.: Intelligent tutoring systems using reinforcement learning to teach autistic students. In: Home Informatics and Telematics: ICT for The Next Billion, pp. 65–78. Springer (2007)
Segall, D.O.: Computerized adaptive testing. Encycl. Soc. Meas. 1, 429–438 (2005)
Sprague, A.: Improving the ESL graduate writing classroom using socrative: (re)considering exit tickets. TESOL J. 7(4), 989–998 (2016)
Thönes, J.: Microservices. IEEE Softw. 32(1), 116–116 (2015)
Trossen, D., Sarela, M., Sollins, K.: Arguments for an information-centric internetworking architecture. SIGCOMM Comput. Commun. Rev. 40(2), 26–33 (2010)
Uram, T.D., Papka, M.E., Hereld, M., Wilde, M.: A solution looking for lots of problems: generic portals for science infrastructure. In: Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery, TG ’11, pp. 44:1–44:7, New York, NY, USA. ACM (2011)
Van der Linden, W.J., Glas, C.A.W.: Computerized Adaptive Testing: Theory and Practice. Springer (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Kiwelekar, A.W., Laddha, M.D., Netak, L.D., Gandhi, S. (2020). An Architectural Perspective of Learning Analytics. In: Virvou, M., Alepis, E., Tsihrintzis, G., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 158. Springer, Cham. https://doi.org/10.1007/978-3-030-13743-4_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-13743-4_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-13742-7
Online ISBN: 978-3-030-13743-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)