Generic adaptation framework for unifying adaptive web-based systems

The Generic Adaptation Framework (GAF) research project first and foremost creates a common formal framework for describing current and future adaptive hypermedia (AHS) and adaptive webbased systems in general. It provides a commonly agreed upon taxonomy and a reference model that encompasses the most general architectures of the present and future, including conventional AHS, and different types of personalization-enabling systems and applications such as recommender systems (RS) personalized web search, semantic web enabled applications used in personalized information delivery, adaptive e-Learning applications and many more. At the same time GAF is trying to bring together two (seemingly not intersecting) views on the adaptation: a classical pre-authored type, with conventional domain and overlay user models and data-driven adaptation which includes a set of data mining, machine learning and information retrieval tools. To bring these research fields together we conducted a number GAF compliance studies including RS, AHS, and other applications combining adaptation, recommendation and search. We also performed a number of real systems’ case-studies to prove the point and perform a detailed analysis and evaluation of the framework. Secondly it introduces a number of new ideas in the field of AH, such as the Generic Adaptation Process (GAP) which aligns with a layered (data-oriented) architecture and serves as a reference adaptation process. This also helps to understand the compliance features mentioned earlier. Besides that GAF deals with important and novel aspects of adaptation enabling and leveraging technologies such as provenance and versioning. The existence of such a reference basis should stimulate AHS research and enable researchers to demonstrate ideas for new adaptation methods much more quickly than if they had to start from scratch. GAF will thus help bootstrap any adaptive web-based system research, design, analysis and evaluation

Assembling learning objects for personalized learning. An AI planning perspective

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.[EN] The aim of educational systems is to assemble learning objects on a set of topics tailored to the goals and individual students' styles. Given the amount of available Learning Objects, the challenge of e-learning is to select the proper objects, define their relationships, and adapt their sequencing to the specific needs, objectives, and background of the student. This article describes the general requirements for course adaptation, the full potential of applying planning techniques on the construction of personalized e-learning routes, and how to accommodate the temporal and resource constraints to make the course applicable in a real scenario.This work has been partially supported by the Spanish Ministerio de Ciencia e Innovacion (MICINN) under projects TIN2008-06701-C03-01, Consolider Ingenio 2010 CSD2007-00022, and the Valencian Prometeo project 2008/051.Garrido, A.; Onaindia De La Rivaherrera, E. (2013). Assembling learning objects for personalized learning. An AI planning perspective. IEEE Intelligent Systems. 28(2):64-73. https://doi.org/10.1109/MIS.2011.36S647328

Evaluation of Neuro-Evolution Algorithms for Tactic Volatility Aware Processes

Our society is increasingly evolving to rely on computer mechanisms that perform a variety of tasks. From a self-driving car to a satellite in space relaying data from Mars rovers, we need these systems to perform optimally and without failure. One such point of failure these systems can encounter is tactic volatility of an adaptation tactic. Adaptation tactics are defined workflows that allow systems to navigate their environment. Tactic volatility is the variance in the behavior in the attribute of a tactic, such as cost and latency and/or the combination of the two. Current systems consider these tactic attributes to be static. Studies have shown that not accounting for tactic volatility can adversely affect a system\u27s ability to operate effectively and resiliently. To support self-adaptive systems and address their limitations, this paper proposes a Tactic Volatility Aware solution that utilizes eRNN (TVA-E) and addresses the limitations of current self-adaptive systems. For this research, we used real-world data that has been made available for use by researchers and academics. This data contains real-world volatility and helps us demonstrate the positive impact TVA-E when used in self-adaptive systems. We also employ the use of uncertainty reduction tactics and how they can assist in accounting for tactic volatility. This work will serve as an evaluation and a comparison of using different machine learning methods to predict and account for tactic volatility. We will study different predictive mechanisms in this paper: Auto-Regressive Moving Average(ARIMA), Evolving Recurrent Neural Network(eRNN), Multi-Layer Perceptron(MLP), and Support Vector Regression(SVR). These methods will be studied with our TVA-E process and we will analyze how they can enhance a self-adaptive system’s performance when it accounts for tactic volatility

Ontology-based personalisation of e-learning resources for disabled students

Students with disabilities are often expected to use e-learning systems to access learning materials but most systems do not provide appropriate adaptation or personalisation to meet their needs.The difficulties related to inadaptability of current learning environments can now be resolved using semantic web technologies such as web ontologies which have been successfully used to drive e-learning personalisation. Nevertheless, e-learning personalisation for students with disabilities has mainly targeted those with single disabilities such as dyslexia or visual impairment, often neglecting those with multiple disabilities due to the difficulty of designing for a combination of disabilities.This thesis argues that it is possible to personalise learning materials for learners with disabilities, including those with multiple disabilities. This is achieved by developing a model that allows the learning environment to present the student with learning materials in suitable formats while considering their disability and learning needs through an ontology-driven and disability-aware personalised e-learning system model (ONTODAPS). A disability ontology known as the Abilities and Disabilities Ontology for Online LEarning and Services (ADOOLES) is developed and used to drive this model. To test the above hypothesis, some case studies are employed to show how the model functions for various individuals with and without disabilities and then the implemented visual interface is experimentally evaluated by eighteen students with disabilities and heuristically by ten lecturers. The results are collected and statistically analysed.The results obtained confirm the above hypothesis and suggest that ONTODAPS can be effectively employed to personalise learning and to manage learning resources. The student participants found that ONTODAPS could aid their learning experience and all agreed that they would like to use this functionality in an existing learning environment. The results also suggest that ONTODAPS provides a platform where students with disabilities can have equivalent learning experience with their peers without disabilities. For the results to be generalised, this study could be extended through further experiments with more diverse groups of students with disabilities and across multiple educational institutions

Models for Learning (Mod4L) Final Report: Representing Learning Designs

The Mod4L Models of Practice project is part of the JISC-funded Design for Learning Programme. It ran from 1 May – 31 December 2006. The philosophy underlying the project was that a general split is evident in the e-learning community between development of e-learning tools, services and standards, and research into how teachers can use these most effectively, and is impeding uptake of new tools and methods by teachers. To help overcome this barrier and bridge the gap, a need is felt for practitioner-focused resources which describe a range of learning designs and offer guidance on how these may be chosen and applied, how they can support effective practice in design for learning, and how they can support the development of effective tools, standards and systems with a learning design capability (see, for example, Griffiths and Blat 2005, JISC 2006). Practice models, it was suggested, were such a resource. The aim of the project was to: develop a range of practice models that could be used by practitioners in real life contexts and have a high impact on improving teaching and learning practice. We worked with two definitions of practice models. Practice models are: 1. generic approaches to the structuring and orchestration of learning activities. They express elements of pedagogic principle and allow practitioners to make informed choices (JISC 2006) However, however effective a learning design may be, it can only be shared with others through a representation. The issue of representation of learning designs is, then, central to the concept of sharing and reuse at the heart of JISC’s Design for Learning programme. Thus practice models should be both representations of effective practice, and effective representations of practice. Hence we arrived at the project working definition of practice models as: 2. Common, but decontextualised, learning designs that are represented in a way that is usable by practitioners (teachers, managers, etc).(Mod4L working definition, Falconer & Littlejohn 2006). A learning design is defined as the outcome of the process of designing, planning and orchestrating learning activities as part of a learning session or programme (JISC 2006). Practice models have many potential uses: they describe a range of learning designs that are found to be effective, and offer guidance on their use; they support sharing, reuse and adaptation of learning designs by teachers, and also the development of tools, standards and systems for planning, editing and running the designs. The project took a practitioner-centred approach, working in close collaboration with a focus group of 12 teachers recruited across a range of disciplines and from both FE and HE. Focus group members are listed in Appendix 1. Information was gathered from the focus group through two face to face workshops, and through their contributions to discussions on the project wiki. This was supplemented by an activity at a JISC pedagogy experts meeting in October 2006, and a part workshop at ALT-C in September 2006. The project interim report of August 2006 contained the outcomes of the first workshop (Falconer and Littlejohn, 2006). The current report refines the discussion of issues of representing learning designs for sharing and reuse evidenced in the interim report and highlights problems with the concept of practice models (section 2), characterises the requirements teachers have of effective representations (section 3), evaluates a number of types of representation against these requirements (section 4), explores the more technically focused role of sequencing representations and controlled vocabularies (sections 5 & 6), documents some generic learning designs (section 8.2) and suggests ways forward for bridging the gap between teachers and developers (section 2.6). All quotations are taken from the Mod4L wiki unless otherwise stated

Digital Transformation of HR Technologies

This article involves a study of the personnel management technologies digitalization degree based on the results of the SAP, Deloitte and Hays report, published in 2019. The analysis demonstrated HR automatization dependence on the size and specialization of organizations. The larger the company, the more complex personnel management processes are. The article defines four digitalization levels from the paper approach to the active use of artificial intelligence systems. Industries with the highest automatization percentage are identified. The article takes a closer look at personnel recruitment, selection, adaptation and training practices performed with the use of appropriate tools and programs. Among the most laborious recruitment functions stand communication with potential candidates, testing and interviewing, especially in cases of mass recruitment. Specialized programs that process CVs, build ratings, conduct video interviews and online testing to optimize recruiter’s work. The advantages of chatbots and messengers for adaptation digitalization are pointed out. Most executives have a positive attitude to the transition to new automated HR methods. The role of e-learning and software training is outlined, and the advantages of webinars, test constructors, and distance learning implementation are analyzed. It is noted that one third of Russian companies are actively automatizing employee training and development technologies. The article also highlights the most important personnel management processes that demand digital transformation in the first place. The automatization necessity of management accounting, effectiveness analysis of the current HR system, and benefits calculation is justified. Gamification advantages are outlined, as they are used in adaptation, training and personnel assessment processes. Senior management, HR executives and information services role in moving to a new level of personnel management is emphasized. VTB, VTB 24 and Sberbank spendings on automated HR systems are reasoned. Conclusions on the need to invest in digital transformation of HR processes are drawn. Keywords: personnel management, technologies, digitalization, proces

Challenges for adaptation in agent societies

The final publication is available at Springer via http://dx.doi.org/[insert DOIAdaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, the European Cooperation in the field of Scientific and Technical Research IC0801 AT, and projects TIN2009-13839-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). Challenges for adaptation in agent societies. Knowledge and Information Systems. 38(1):1-34. https://doi.org/10.1007/s10115-012-0565-yS134381Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Abdallah S, Lesser V (2007) Multiagent reinforcement learning and self-organization in a network of agents. In: Proceedings of the sixth international joint conference on autonomous agents and multi-agent systems, pp 172–179Abdu H, Lutfiyya H, Bauer MA (1999) A model for adaptive monitoring configurations. In: Proceedings of the VI IFIP/IEEE IM conference on network management, pp 371–384Alberola JM, Julian V, Garcia-Fornes A (2011) A cost-based transition approach for multiagent systems reorganization. In: Proceedings of the 10th international conference on aut. agents and MAS (AAMAS11), pp 1221–1222Alberola JM, Julian V, Garcia-Fornes A (2012) Multi-dimensional transition deliberation for organization adaptation in multiagent systems. In: Proceedings of the 11th international conference on aut. agents and MAS (AAMAS12) (in press)Argente E, Julian V, Botti V (2006) Multi-agent system development based on organizations. Electron Notes Theor Comput Sci 160(3):55–71Argente E, Botti V, Carrascosa C, Giret A, Julian V, Rebollo M (2011) An abstract architecture for virtual organizations: the Thomas approach. Knowl Inf Syst 29(2):379–403Ashford SJ, Taylor MS (1990) Adaptation to work transitions. An integrative approach. Res Pers Hum Resour Manag 8:1–39Ashford SJ, Blatt R, Walle DV (2003) Reflections on the looking glass: a review of research on feedback-seeking behavior in organizations. J Manag 29(6):773–799Astley WG, Van de Ven AH (1983) Central perspectives and debates in organization theory. Adm Sci Q 28(2):245–273Bond AH, Gasser L (1988) A survey of distributed artificial intelligence readings in distributed artificial intelligence. Morgan Kaufmann, Los AltosBou E, López-Sánchez M, Rodríguez-Aguilar JA (2006) Adaptation of autonomic electronic institutions through norms and institutional agents In: Engineering societies in the agents world. Number LNAI 445, Springer, Dublin, pp 300–319Bou E, López-Sánchez M, Rodríguez-Aguilar JA (2007) Towards self-configuration in autonomic electronic institutions. In: COIN 2006 workshops. Number LNAI 4386, pp 220–235Bou E, López-Sánchez M, Rodríguez-Aguilar JA (2008) Using case-based reasoning in autonomic electronic institutions. In: Proceedings of the 2007 international conference on coordination, organizations, institutions, and norms in agent systems III, pp 125–138Brett JM, Feldman DC, Weingart LR (1990) Feedback-seeking behavior of new hires and job changers. J Manag 16:737–749Bulka B, Gaston ME, desJardins M (2007) Local strategy learning in networked multi-agent team formation. Auton Agents Multi-Agent Syst 15(1):29–45Campos J, López-Sánchez M, Esteva M (2009) Assistance layer, a step forward in multi-agent systems. In: Coordination support international joint conference on autonomous agents and multiagent systems (AAMAS), pp 1301–1302Campos J, Esteva M, López-Sánchez M, Morales J, Salamó M (2011) Organisational adaptation of multi-agent systems in a peer-to-peer scenario. Computing 91(2):169–215Carley KM, and Gasser L (1999) Computational organization theory. Multiagent systems: a modern approach to distributed artificial intelligence. MIT Press, Cambridge, pp 299–330Carvalho G, Almeida H, Gatti M, Vinicius G, Paes R, Perkusich, A, Lucena C (2006) Dynamic law evolution in governance mechanisms for open multi-agent systems. In: Second workshop on software engineering for agent-oriented systemsCernuzzi L, Zambonelli F (2011) Adaptive organizational changes in agent-oriented methodologies. Knowl Eng Rev 26(2):175–190Cheng BH, Lemos R, Giese H, Inverardi P, Magee J (2009) Software engineering for self-adaptive systems: a research roadmap, pp 1–26Corkill DD, Lesser VR (1983) The use of meta-level control for coordination in a distributed problem solving networks. In: Proceedings of the eighth international joint conference on artificial intelligence. IEEE Computer Society Press, pp 748–756Corkill DD, Lander SE (1998) Diversity in agent organizations. Object Mag 8(4):41–47de Paz JF, Bajo J, González A, Rodríguez S, Corchado JM (2012) Combining case-based reasoning systems and support vector regression to evaluate the atmosphere-ocean interaction. Knowl Inf Syst 30(1):155–177DeLoach SA, Matson E (2004) An organizational model for designing adaptive multiagent systems. In: The AAAI-04 workshop on agent organizations: theory and practice (AOTP), pp 66–73DeLoach SA, Oyeman W, Matson E (2008) A capabilities-based model for adaptive organizations. Auton Agents Multi-Agent Syst 16:13–56Dignum V, Dignum F (2001) Modelling agent societies: co-ordination frameworks and institutions progress in artificial intelligence. LNAI 2258, pp 191–204Dignum V (2004) A model for organizational interaction: based on agents, founded in logic. PhD dissertation, Universiteit Utrecht. SIKS dissertation series 2004-1Dignum V, Dignum F, Sonenberg L (2004) Towards dynamic reorganization of agent societies. In: Proceedings of the workshop on coordination in emergent agent societies, pp 22–27Dignum V, Dignum F (2006) Exploring congruence between organizational structure and task performance: a simulation approach coordination, organization, institutions and norms in agent systems I. In: Proceedings of the ANIREM ’05/OOOP ’05, pp 213–230Dignum V, Dignum F (2007) A logic for agent organizations. In: Proceedings of the multi-agent logics, languages, and organisations federated workshops (MALLOW ’007), formal approaches to multi-agent systems (FAMAS ’007) workshopFox MS (1981) Formalizing virtual organizations. IEEE Transact Syst Man Cybern 11(1):70–80Gaston ME, desJardins M (2005) Agent-organized networks for dynamic team formation. In: Proceedings of the fourth international joint conference on autonomous agents and multiagent systems, pp 230–237Gaston ME, desJardins M (2008) The effect of network structure on dynamic team formation in multi-agent systems. Comput Intell 24(2):122–157Norbert G, Philippe M (1997) The reorganization of societies of autonomous agents. In: MAAMAW-97. Springer, London, pp 98–111Goldman CV, Rosenschein JS (1997) Evolving organizations of agents American association for artificial intelligence. In: Multiagent learning workshop at AAAI97Greve HR (1998) Performance, aspirations, and risky organizational change. Adm Sci Quart 43(1):58–86Guessoum Z, Ziane M, Faci N (2004) Monitoring and organizational-level adaptation of multi-agent systems. In: Proceedings of the AAMAS ’04, pp 514–521Hoogendoorn M, Treur J (2006) An adaptive multi-agent organization model based on dynamic role allocation. In: Proceedings of the IAT ’06, pp 474–481Horling B, Benyo B, Lesser V (1999) Using self-diagnosis to adapt organizational structures. In: Proceedings of the 5th international conference on autonomous agents, pp 529–536Horling B, Lesser V (2005) A survey of multi-agent organizational paradigms. Knowl Eng Rev 19(4): 281–316Hrebiniak LG, Joyce WF (1985) Organizational adaptation: strategic choice and environmental determinism. Adm Sci Quart 30(3):336–349Hübner JF, Sichman JS, Boissier O (2002) MOISE+: towards a structural, functional, and deontic model for MAS organization. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems, pp 501–502Hübner JF, Sichman JS, Boissier O (2004) Using the MOISE+ for a cooperative framework of MAS reorganisation. In: Proceedings of the 17th Brazilian symposium on artificial intelligence (SBIA ’04), vol 3171, pp 506–515Hübner JF, Boissier O, Sichman JS (2005) Specifying E-alliance contract dynamics through the MOISE + reorganisation process Anais do V Encontro Nacional de Inteligde Inteligncia Artificial (ENIA 2005)Jennings NR (2001) An agent-based approach for building complex software systems. Commun ACM 44(4):35–41Kamboj S, Decker KS (2006) Organizational self-design in semi-dynamic environments In: 2007 IJCAI workshop on agent organizations: models and simulations (AOMS@IJCAI), pp 335–337Katz D, Kahn RL (1966) The social psychology of organizations. Wiley, New YorkKelly D, Amburgey TL (1991) Organizational inertia and momentum: a dynamic model of strategic change. Acad Manag J 34(3):591–612Kephart J, Chess DM (2003) The vision of autonomic computing. Computer 36(1):41–50Kim DH (1993) The link between individual and organizational learning. Sloan Manag Rev 35(1):37–50Kota R, Gibbins N, Jennings NR (2009a) Decentralised structural adaptation in agent organisations organized adaptation in multi-agent systems, pp 54–71Kota R, Gibbins N, Jennings NR (2009b) Self-organising agent organisations. In: Proceedings of the 8th international conference on autonomous agents and multiagent systems (AAMAS 2009)Kota R, Gibbins N, Jennings NR (2012) Decentralised approaches for self-adaptation in agent organisations. ACM Trans Auton Adapt Syst 7(1):1–28Kotter J, Schlesinger L (1979) Choosing strategies for change. Harv Bus Rev 106–1145Lesser VR (1998) Reflections on the nature of multi-agent coordination and its implications for an agent architecture. Auton Agents Multi-Agent Syst 89–111Levitt B, March JG (1988) Organizational learning. Annu Rev Sociol 14:319–340Luck M, McBurney P, Shehory O, Willmott S (2005) Agent technology: computing as interaction (a roadmap for agent based computing)Mathieu P, Routier JC, Secq Y (2002a) Dynamic organization of multi-agent systems. In: Proceedings of the first international joint conference on autonomous agents and multiagent systems: part 1, pp 451–452Mathieu P, Routier JC, Secq Y (2002b) Principles for dynamic multi-agent organizations. In: Proceedings of the 5th Pacific rim international workshop on multi agents: intelligent agents and multi-agent systems, pp 109–122Matson E, DeLoach S (2003) Using dynamic capability evaluation to organize a team of cooperative, autonomous robots. In: Proceedings of the 2003 international conference on artificial intelligence (IC-AI ’03), Las Vegas, pp 23–26Matson E, DeLoach S (2004) Enabling intra-robotic capabilities adaptation using an organization-based multiagent system. ICRA, pp 2135–2140Matson E, DeLoach S (2005) Formal transition in agent organizations. In: IEEE international conference on knowledge intensive multiagent systems (KIMAS ’05)Matson E, Bhatnagar R (2006) Properties of capability based agent organization transition. In: Proceedings of the IEEE/WIC/ACM international conference on intelligent agent technology IAT ’06, pp 59–65Morales J, López-Sánchez M, Esteva, M (2011) Using experience to generate new regulations. In: Proceedings of the twenty-second international joint conference on artificial Intelligence (IJCAI-11), pp 307–312Muhlestein D, Lim S (2011) Online learning with social computing based interest sharing. Knowl Inf Syst 26(1):31–58Nair R, Tambe M, Marsella S (2003) Role allocation and reallocation in multiagent teams: towards a practical analysis. In: Proceedings of the second AAMAS ’03, pp 552–559Orlikowski WJ (1996) Improvising organizational transformation over time: a situated change perspective. Inf Syst Res 7(1):63–92Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agents Multi-Agent Syst 11:387–434Ringold PL, Alegria J, Czaplewski RL, Mulder BS, Tolle T, Burnett K (1996) Adaptive monitoring design for ecosystem management. Ecol Appl 6(3):745–747Routier J, Mathieu P, Secq Y (2001) Dynamic skill learning: a support to agent evolution. In: Proceedings of the artificial intelligence and the simulation of behaviour symposium on adaptive agents and multi-agent systems (AISB ’01), pp 25–32Scott RW (2002) Organizations: rational, natural, and open systems, 5th edn. Prentice Hall International, New YorkSeelam A (2009) Reorganization of massive multiagent systems: MOTL/O http://books.google.es/books?id=R-s8cgAACAAJ . Southern Illinois University CarbondaleSo Y, Durfee EH (1993) An organizational self-design model for organizational change. In: AAAI93 workshop on AI and theories of groups and oranizations, pp 8–15So Y, Durfee EH (1998) Designing organizations for computational agents. Simulating organizations. MIT Press, Cambridge, pp 47–64Schwaninger M (2000) A theory for optimal organization. Technical report. Institute of Management at the University of St. Gallen, SwitzerlandTantipathananandh C, Berger-Wolf TY (2011) Finding communities in dynamic social networks. In: IEEE 11th international conference on data mining 2011, pp 1236–1241Wang Z, Liang X (2006) A graph based simulation of reorganization in multi-agent systems. In: IEEE WICACM international conference on intelligent agent technology, pp 129–132Wang D, Tse Q, Zhou Y (2011) A decentralized search engine for dynamic web communities. Knowl Inf Syst 26(1):105–125Weick KE (1979) The social psychology of organizing, 2nd edn. Addison-Wesley, ReadingWeyns D, Haesevoets R, Helleboogh A, Holvoet T, Joosen W (2010a) The MACODO middleware for context-driven dynamic agent organizations. ACM Transact Auton Adapt Syst 3:1–3:28Weyns D, Malek S, Andersson J (2010b) FORMS: a formal reference model for self-adaptation. In: Proceedings of the 7th international conference on autonomic computing, pp 205–214Weyns D, Georgeff M (2010) Self-adaptation using multiagent systems. IEEE Softw 27(1):86–91Zhong C (2006) An investigation of reorganization algorithms. Master-thesi

The higher education management in medical universities during the COVID-19 pandemic

Background: The use of electronic technology plays a key role in the change in higher education management. This study aimed to assess the necessity of adaptation of electronic learning systems management during the COVID-19 pandemic. Methods: The present study was mixed research. Its statistical population in the qualitative section included 50 experts in higher education management of medical universities. The statistical population in the quantitative section included 242 department heads of 65 medical universities selected according to Morgan's table. Purposeful sampling was used in the qualitative section and cluster random in the quantitative section. The interview was used in the qualitative section and a researcher-made questionnaire was used in the quantitative section. Qualitative data analysis was performed with MAXQDA 2019 software and quantitative data analysis was performed with SPSS software. Results: In the qualitative section, 9 general categories were obtained. In the quantitative section, the results of the one-sample t-test in the dimensions of development of technology and electronic service, expansion of virtual and integrated education, enhancing the quality of learning, expanding research, access to scientific resources, the efficiency of the educational system and optimization of capital and financial affairs of the current status of higher education management in medical universities were determined. Conclusion: For the development of e-learning at the university level during the COVID-19 pandemic, it is necessary to know the motivating factors and barriers well and use the gained experience to select appropriate strategies to accelerate the development process of e-learning

The design and implementation of an adaptive e-learning system

This paper describes the design and implementation of an adaptive e-learning system that provides a template for different learning materials as well as a student model that incorporates five distinct student characteristics as an aid to learning: primary characteristics are prior knowledge, learning style and the presence or absence of animated multimedia aids (multimedia mode); secondary characteristics include page background preference and link colour preference. The use of multimedia artefacts as a student characteristic has not previously been implemented or evaluated. The system development consists of a requirements analysis, design and implementation. The design models including use case diagrams, conceptual design, sequence diagrams, navigation design and presentation design are expressed using Unified Modelling Language (UML). The adaptive e-learning system was developed in a template implemented using Java Servlets, XHTML, XML, JavaScript and HTML. The template is a domain-independent adaptive e-learning system that has functions of both adaptivity and adaptability

Layered evaluation of interactive adaptive systems : framework and formative methods

Peer reviewedPostprin