Smart LMS
By Manuel Gentile and Giuseppe Città, Istituto per le Tecnologie Didattiche - Consiglio Nazionale delle Ricerche
E-learning and Learning Management Systems (LMS)
The number of people making use of e-learning is constantly growing. The term e-learning refers to learning mediated by the use of technology in contexts where educators and learners are distant in space and/or time. The ultimate aim of e-learning is to improve students’ learning experience and practice.Today, with the advancement of technology it is more appropriate to refer to systems and platforms for the "delivery" of e-learning rather than to single tools. Such systems are the result of the integration of different software tools capable of building an ecosystem where flexible and adaptable learning paths can be exploited. An e-learning system enables the management of learning processes and the management of courses. It enables to carry out of student learning assessments, the construction of reports, and the creation of contents and their organisation. It facilitates communication between teachers/tutors and students. Among the most widely used e-learning systems, there are Learning Management Systems (LMS) (e.g. Moodle, Edmodo).
The acronym LMS refers to a web-based application designed to manage the learning process of trainees [1] at different levels, in different domains and different ways. An LMS, therefore, could be defined as a learning environment within which learning activities and tools, assessment activities and tools, learning content, and student-student and/or student-educator interactions are implemented and managed. Furthermore, the definition of LMSs includes their being platforms that generally can include whole course management systems, content management systems, and portals [2].
LMS and AI: the Smart LMS
With the advent of AI, Education, in general, and LMSs, in particular, become possible and promising fields of application of this revolutionary force [3]. Specifically, LMSs, thanks to the functionalities supported by AI, represent a renewed learning tool capable of satisfying two of the fundamental traits of the education of the future: personalisation and adaptation [4].It is from this combination of LMS and AI that the Smart LMS (SLMS) or Intelligent LMS emerges.Generally speaking, an efficient SLMS is a system whose algorithms can provide-information-and-retrieve-information-from three fundamental clusters of knowledge: a) the learner b) the pedagogy, and c) the domain. By acquiring information about (a) learners' preferences, their emotional and cognitive states, their achievements and goals, an SLMS can implement those teaching strategies (b) that are most effective (specific types of assessment, collaborative learning, etc.) for learning to be most fruitful within the specific domain of knowledge being studied (c): e.g. geometry theorems, mathematical operations, laws of physics, text analysis procedures [4].
An SLMS, therefore, can be defined as a learning system capable of adapting the contents proposed to the learner by calibrating them to the knowledge and skills the learner has displayed in previous tasks. In fact, by adopting a learner-centred approach, it can identify, follow and monitor learners' paths by recording their learning patterns and styles. Referring to the description given by Fardinpour et al.[5], an intelligent LMS provides the learner with the most effective learning path and the most appropriate learning content, through automation, the adaptation of different teaching strategies (scaffolding), the reporting and knowledge generation. It also provides the learners with the possibility to keep track of and monitor their learning and learning goals. Furthermore, although these features and tools enable the LMS to operate more intelligently, an SLMS must provide learners with the possibility to disable the AI that manages their path to have full access to all learning materials in the learning environment.
Some examples of AI-supported functionalities in the context of an SLMS
In the actual practice of an SLMS functioning, several AI-supported tools make it possible to realise a system with the features described above. Such AI-supported tools move transversally along the three aforementioned clusters of knowledge to which the SLMS algorithms constantly refer (learner, pedagogy, domain).AI-supported chatbots as virtual tutors
A chatbot - a software that simulates and processes human conversations (written or spoken) - in the context of an SLMS can perform the function of a virtual tutor capable, on the one hand, of answering questions that learners have concerning learning courses. On the other hand, it is capable of providing suggestions to the learner based on the analysis that the system makes of the learner's previous performances and interactions [6].
Learning Analytics
Learning Analytics - data relating to the details of individual learner interactions in online learning activities - allow teachers to monitor learner progress and performance in depth. Thanks to them, the system can implement automatic computer-assisted educational task activation [7] to supplement the activities of learners who have shown performance deficits in specific tasks. In addition, it can automatically provide suggestions to teaching staff regarding the difficulty of proposed tasks or the need to supplement them with additional learning content.
Benefits for learners and teachers
These and other AI-supported tools [4] contribute to making an SLMS a powerful learning and teaching tool that, instead of being perceived as a substitute for the teacher's work, shows itself as a tool capable of "augmenting" the human aspects of teaching [8] and bringing a series of fundamental benefits to the whole learning/teaching process.
Since an SLMS calibrates the contents to the student's skills and level, it avoids the learner facing, in the different phases of his or her path, tasks that bore him or her because they are too simple, or that frustrate him or her because they are too complex. This ensures that the student's motivation and attention are always at a high level and appropriate to the level of difficulty of the task to be addressed. This situation has the direct consequence of significantly reducing the dropout rate, as it allows teachers to detect any problems in time and intervene promptly as soon as the student shows the first signs of difficulty.
Such a situation, as well as linear learning situations (without difficulties), can be addressed by proposing to the students, through the SLMS tools, different knowledge contents already stored in the course databases or also coming from third-party providers. This results in a direct benefit for the teacher who does not have to create new teaching materials from time to time and can use the saved time in other essential occupations such as refining their teaching methods and/or interacting directly with the students.
Reference
[1] Kasim, N. N. M., & Khalid, F. (2016). Choosing the right learning management system (LMS) for the higher education institution context: A systematic review. International Journal of Emerging Technologies in Learning, 11(6).[2] Coates, H., James, R., & Baldwin, G. (2005). A critical examination of the effects of learning management systems on university teaching and learning. Tertiary education and management, 11(1), 19-36.
[3] Beck, J., Sternm, M., & Haugsjaa, E. (1996). Applications of AI in Education. Crossroads, 3(1), 11–15. doi:10.1145/332148.332153
[4] Rerhaye, L., Altun, D., Krauss, C., & Müller, C. (2021, July). Evaluation Methods for an AI-Supported Learning Management System: Quantifying and Qualifying Added Values for Teaching and Learning. In International Conference on Human-Computer Interaction (pp. 394-411). Springer, Cham.
[5] Fardinpour, A., Pedram, M. M., & Burkle, M. (2014). Intelligent learning management systems: Definition, features and measurement of intelligence. International Journal of Distance Education Technologies (IJDET), 12(4), 19-31.
[6] HR Technologist: Emerging Trends for AI in Learning Management Systems (2019).https://www.hrtechnologist.com/articles/learning-development/emerging-trends-for- ai-in-the-learning-management-system/. Accessed 31 Oct 2022
[7] Krauss, C., Salzmann, A., & Merceron, A. (2018, September). Branched Learning Paths for the Recommendation of Personalized Sequences of Course Items. In DeLFI Workshops.
[8] Mavrikis, M., & Holmes, W. (2019). Intelligent learning environments: Design, usage and analytics for future schools. Shaping future schools with digital technology, 57-73.