Layered evaluation of interactive adaptive systems: framework and formative methods
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This is a well known issue in evaluating interactive adaptive systems.
“This is a well known issue in evaluating interactive adaptive systems (c.f. Paramythis et al., 2010).”
From Affective learning: Improving engagement and enhancing learning with affect-aware feedback (2017) · cites this paper as Paramythis et al., 2010
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CTRL: A research framework for providing adaptive collaborative learning support
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Understanding and Dealing With Usability Side Effects of Intelligent Processing
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Sensors Model Student Self Concept in the Classroom
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Composing letters with a simulated listening typewriter
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A Decomposition Model for the Layered Evaluation of Interactive Adaptive Systems
Paramythis, A., & Weibelzahl, S. (2005) · Lecture notes in computer science · doi:10.1007/11527886_60 · Cited by 44 · see citing works
Self-citationEvaluation of Adaptive Systems
Weibelzahl, S. (2001) · Lecture notes in computer science · doi:10.1007/3-540-44566-8_49 · Cited by 44 · see citing works
Self-citationA stroll with Carletto: adaptation in drama-based tours with virtual characters
Damiano, R., Cristina Gena, A., Lombardo, V., Nunnari, F., & Pizzo, A. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9053-1 · Cited by 43 · see citing works
Nückles, M., Winter, A., Wittwer, J., Herbert, M., & Hübner, S. (2006) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-006-9000-y · Cited by 43 · see citing works
Adaptive feedback generation to support teachers in web-based distance education
Kosba, E., Dimitrova, V. G., & Boyle, R. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-007-9031-z · Cited by 42 · see citing works
The Effectiveness of Personalized Movie Explanations: An Experiment Using Commercial Meta-data
Tintarev, N., & Masthoff, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70987-9_23 · Cited by 41 · see citing works
Self-citationAdaptive testing for hierarchical student models
Guzmán, E., Conejo, R., & Pérez-de-la-Cruz, J.-L. (2006) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-006-9018-1 · Cited by 40 · see citing works
Limongelli, C., Sciarrone, F., & Vaste, G. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70987-9_16 · Cited by 39 · see citing works
Smart Indicators on Learning Interactions
Glahn, C., Specht, M. M., & Koper, R. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-75195-3_5 · Cited by 39 · see citing works
Evaluating the Inference Mechanism of Adaptive Learning Systems
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Self-citationPEACH - Intelligent Interfaces for Museum Visits (Cognitive Technologies)
Stock, O., & Zancanaro, M. (2007) · Springer eBooks · Cited by 36 · see citing works
A Scrutable Adaptive Hypertext
Czarkowski, M., & Kay, J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47952-x_43 · Cited by 36 · see citing works
Adaptive Evolutionary Information Systems
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Configuration agents, control and privacy
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Logic-Based Representation and Reasoning for User Modeling Shell Systems
Pohl, W. (1999) · User Modeling and User-Adapted Interaction · doi:10.1023/a:1008325713804 · Cited by 34 · see citing works
A User Interface Integrating Adaptability and Adaptivity
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Human or Computer? AutoTutor in a Bystander Turing Test
The Tutoring Research Group, Person, N. K., & Graesser, A. C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47987-2_82 · Cited by 33 · see citing works
The Evaluation of Adaptive Systems
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A multifactor approach to student model evaluation
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The user as wizard: A method for early involvement in the design and evaluation of adaptive systems
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A Comprehensive Typology for Program Evaluation
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Formative Qualitative Evaluation for "Exploratory" ITS Research.
Murray, T. (1993) · Cited by 29 · see citing works
A Comprehensive Model of Usability
Winter, S., Wagner, S., & Deissenboeck, F. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-92698-6_7 · Cited by 28 · see citing works
Horvitz, E., & Paek, T. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-006-9026-1 · Cited by 27 · see citing works
Efficient and non-parametric reasoning over user preferences
Domshlak, C., & Joachims, T. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-006-9022-5 · Cited by 27 · see citing works
Adaptation as a Problem of Design
Totterdell, P., & Rautenbach, P. (1990) · Elsevier eBooks · doi:10.1016/b978-0-12-137755-7.50008-x · Cited by 26 · see citing works
Towards Inferring Sequential-Global Dimension of Learning Styles from Mouse Movement Patterns
Spada, D., Sánchez-Montañés, M. A., Paredes, P., & Carro, R. M. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-70987-9_48 · Cited by 23 · see citing works
Evaluating the Impact of Adaptation to Learning Styles in a Web-Based Educational System
Popescu, E. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03426-8_41 · Cited by 23 · see citing works
The Continuous Empirical Evaluation Approach: Evaluating Adaptive Web-Based Courses
Ortigosa, Á., & Carro, R. M. (2003) · Lecture notes in computer science · doi:10.1007/3-540-44963-9_22 · Cited by 22 · see citing works
Utility-Based Evaluation of Adaptive Systems
Herder, E. (2003) · University of Twente Research Information · Cited by 22 · see citing works
The Evaluation of Adaptive Systems
Masthoff, J. (2003) · IGI Global eBooks · doi:10.4018/978-1-59140-034-9.ch015 · Cited by 21 · see citing works
Self-citationWhen and Why Does Mastery Learning Work: Instructional Experiments with ACT-R “SimStudents”
MacLaren, B., & Koedinger, K. (2002) · Lecture Notes in Computer Science · doi:10.1007/3-540-47987-2_39 · Cited by 20 · see citing works
Tailoring Automatically Generated Hypertext
Bontcheva, K., & Wilks, Y. A. (2005) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-004-5637-6 · Cited by 19 · see citing works
On Evaluating Adaptive Systems for Education
Arruabarrena, R., Pérez, T. A., López-Cuadrado, J., Gutiérrez, J., & Vadillo, J. A. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47952-x_38 · Cited by 17 · see citing works
Introduction to the special issue on statistical and probabilistic methods for user modeling
Albrecht, D. W., & Zukerman, I. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-006-9025-2 · Cited by 17 · see citing works
LaboUr - Machine Learning for User Modeling.
Pohl, W. (1997) · Cited by 16 · see citing works
Modelling a Receiver’s Position to Persuasive Arguments
Nguyen, H. P. N., Masthoff, J., & Edwards, P. J. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-77006-0_33 · Cited by 15 · see citing works
Self-citationUser Modeling Meets Usability Goals
Jameson, A. (2005) · Lecture notes in computer science · doi:10.1007/11527886_1 · Cited by 15 · see citing works
Personalizing influence diagrams: applying online learning strategies to dialogue management
Chickering, D. M., & Paek, T. (2006) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-006-9020-7 · Cited by 15 · see citing works
Designing and Redesigning an Affective Interface for an Adaptive Museum Guide
Goren‐Bar, D., Graziola, I., Rocchi, C., Pianesi, F., Stock, O., & Zancanaro, M. (2005) · Lecture notes in computer science · doi:10.1007/11573548_120 · Cited by 12 · see citing works
Dumas, J. S., & Loring, B. A. (2008) · Cited by 11 · see citing works
Layered Evaluation of Adaptive Search
Brusilovsky, P. L., Farzan, R., & Ahn, J. (2006) · Cited by 10 · see citing works
Tintarev, N., & Masthoff, J. (2009) · Cited by 10 · see citing works
Self-citationPersuasion Artifices to Promote Wellbeing
de Rosis, F., Mazzotta, I., Miceli, M., & Poggi, I. (2006) · Lecture notes in computer science · doi:10.1007/11755494_12 · Cited by 9 · see citing works
Preface to the special issue on data mining for personalization
Mobasher, B., & Tuzhilin, A. (2008) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-008-9060-2 · Cited by 9 · see citing works
Ley, T., Kump, B., Antonia, M., Maiden, N., & Albert, D. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02247-0_32 · Cited by 9 · see citing works
Agent-Based Group Modelling for Ambient Intelligence
Masthoff, J., Vasconcelos, W. W., Aitken, C., & da Silva, F. S. C. (2007) · Cited by 9 · see citing works
Self-citationPredicting time-sharing in mobile interaction
Miettinen, M., & Oulasvirta, A. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-007-9033-x · Cited by 8 · see citing works
What Do You Want to Know? Investigating the Information Requirements of Patient Supporters
Moncur, W., Masthoff, J., & Reiter, E. (2008) · IEEE International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms.2008.36 · Cited by 8 · see citing works
Self-citationDiscovering stages in web navigation for problem-oriented navigation support
Hollink, V., van Someren, M., & Wielinga, B. J. (2006) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-006-9017-2 · Cited by 7 · see citing works
Totterdell, P., Rautenbach, P., Wilkinson, A., & Anderson, S. O. (1990) · Elsevier eBooks · doi:10.1016/b978-0-12-137755-7.50010-8 · Cited by 7 · see citing works
AnAmeter: The First Steps to Evaluating Adaptation
Tarpin-Bernard, F., Marfisi-Schottman, I., & Habieb-Mammar, H. (2009) · HAL · Cited by 7 · see citing works
Developing Instrument for Handset Usability Evaluation: A Survey Study
Zhang, T., Rau, P. P., & Salvendy, G. (2007) · Springer eBooks · doi:10.1007/978-3-540-73105-4_73 · Cited by 6 · see citing works
Inferences, suppositions and explanatory extensions in argument interpretation
George, S., Zukerman, I., & Niemann, M. (2007) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-007-9034-9 · Cited by 6 · see citing works
Guiding Learners in Learning Management Systems through Recommendations
Santos, O. C., & Boticario, J. G. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04636-0_54 · Cited by 5 · see citing works
Discovering Stages in Web Navigation
Hollink, V., van Someren, M., & ten Hagen, S. (2005) · Lecture notes in computer science · doi:10.1007/11527886_65 · Cited by 5 · see citing works
Parallel Presentations for Heterogenous User Groups – An Initial User Study
Kruppa, M., & Aslan, I. (2005) · Lecture notes in computer science · doi:10.1007/11590323_6 · Cited by 4 · see citing works
Assessing the Impact of Measurement Uncertainty on User Models in Spatial Domains
Schmidt, D. F., Zukerman, I., & Albrecht, D. W. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-02247-0_21 · Cited by 4 · see citing works
Ley, T., Kump, B., Antonia, M., Maiden, N., & Albert, D. (2009) · Cited by 4 · see citing works
An Evaluation of the Accuracy of Capturing User Intent for Information Retrieval.
Nguyen, H. P. N., & Santos, E. (2007) · Cited by 3 · see citing works
Intelligent Support to the Retrieval of Information About Hydric Resources
Gena, C., & Ardissono, L. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27780-4_16 · Cited by 3 · see citing works
Formal models and techniques in human-computer interaction
Ziegler, J., & Bullinger, H. (1991) · Cited by 3 · see citing works
Assessment on the Adaptivity of Adaptive Systems
Yang, D., & Hongmei, H. (2008) · International Conference on Management of e-Commerce and e-Government · doi:10.1109/icmecg.2008.85 · Cited by 2 · see citing works
Flagged first, then by citations
1 of the 201 references couldn’t be matched to a known paper.
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Explaining the user experience of recommender systems
Knijnenburg, B. P., Willemsen, M. C., Gantner, Z., Soncu, H., & Newell, C. (2012) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9118-4 · Cited by 799 · see citing works
Evaluating recommender systems from the user’s perspective: survey of the state of the art
Pu, P., Chen, L., & Hu, R. J. (2012) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9115-7 · Cited by 377 · see citing works
“This method is called layered evaluation in Paramythis et al. (2010).”
MethodEvaluating the effectiveness of explanations for recommender systems
Tintarev, N., & Masthoff, J. (2012) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9117-5 · Cited by 334 · see citing works
“We used the layered evaluation framework, which advocates that adaptation needs to be decomposed and assessed in layers in order to be evaluated effectively (Paramythis et al. 2010).”
MethodSelf-citationPersonalization in cultural heritage: the road travelled and the one ahead
Ardissono, L., Kuflik, T., & Petrelli, D. (2012) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-011-9104-x · Cited by 257 · see citing works
“As for all other aspects of personalization research, the system and user evaluation would benefit from a common effort and the creation of a shared understanding of which new systems should be evaluated, and how (Weibelzahl, 2002; Paramythis et al., 2010).”
BackgroundSusan Bull, A., & Kay, J. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-015-0090-8 · Cited by 165 · see citing works
“…J paper is cited as indicated in Table 3, this has mostly been as a reference to the existence of the framework (e.g. Brusilovsky et al., 2011; Paramythis et al., 2010), as a detailed review and/or as giving examples of open learner models (e.g. Hsiao & Brusilovsky, 2012; Kump et al., 2012),…”
BackgroundMethodEvaluating Recommender Systems for Technology Enhanced Learning: A Quantitative Survey
Erdt, M., Fernández, A., & Rensing, C. (2015) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2015.2438867 · Cited by 146 · see citing works
Evaluation of Adaptive Systems
Weibelzahl, S., Paramythis, A., & Masthoff, J. (2020) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3340631.3398668 · Cited by 122 · see citing works
Self-citationNakić, J., Granić, A., & Glavinić, V. (2015) · Journal of Educational Computing Research · doi:10.2190/ec.51.4.e · Cited by 114 · see citing works
“Evaluation of adaptive systems needs to involve end-users and has to be specifically designed to address all aspects of adaptivity (Paramythis et al, 2010; van Velsen et al., 2008).”
BackgroundMethodInfluentialAffective learning: Improving engagement and enhancing learning with affect-aware feedback
Grawemeyer, B., Mavrikis, M., Holmes, W., Gutiérrez-Santos, S., Wiedmann, M., & Rummel, N. (2017) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-017-9188-z · Cited by 105 · see citing works
“This is a well known issue in evaluating interactive adaptive systems (c.f. Paramythis et al., 2010).”
Training the Body: The Potential of AIED to Support Personalized Motor Skills Learning
Santos, O. C. (2016) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-016-0103-2 · Cited by 92 · see citing works
“TORMES combines user centred design methods with data mining techniques and extends the design cycle of interactive systems as defined by ISO 9241–210 (ISO 2010) with the life cycle of e-learning (Van Rosmalen et al. 2004) and the layered evaluation of adaptive systems (Paramythis et al. 2010).”
MethodSantos, O., Boticario, J., & Pérez-Marín, D. (2014) · Science of Computer Programming · doi:10.1016/j.scico.2013.12.004 · Cited by 80 · see citing works
“However, some generic frameworks for interactive adaptive systems have being defined following a layered evaluation approach, where adaptation is broken down into its constituents (called layers) and each of these layers is evaluated separately [113].”
BackgroundModel-based intelligent user interface adaptation: challenges and future directions
Abrahão, S., Insfran, E., Sluÿters, A., & Vanderdonckt, J. (2021) · Software and Systems Modeling · doi:10.1007/s10270-021-00909-7 · Cited by 74 · see citing works
Practical guidelines for designing and evaluating educationally oriented recommendations
Santos, O. C., & Boticario, J. G. (2014) · Computers & Education · doi:10.1016/j.compedu.2014.10.008 · Cited by 74 · see citing works
“…approach), there are other methodologies that impinge on the design process, namely the e-learning life cycle of personalised educational systems proposed in (Van Rosmalen et al., 2004) and the layered evaluation of the corresponding adaptation features as compiled in (Paramythis et al., 2010).”
BackgroundCreating personalized systems that people can scrutinize and control
Kay, J., & Kummerfeld, B. (2012) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2395123.2395129 · Cited by 72 · see citing works
“Similarly Paramythis, Weibelzahl and Masthoff identify scrutability as one of the criteria on which to evaluate personalised systems [Paramythis et al. 2010].”
BackgroundUsability and User Experience Evaluation in Intelligent Environments: A Review and Reappraisal
Ntoa, S. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2394724 · Cited by 99 · see citing works
“A comprehensive model for Interactive Adaptive Systems (IAS), taking into account all the previous layered evaluation models, was proposed by Paramythis et al. (2010), according to which the main levels of adaptation are collection of input data, interpretation of the collected data, modeling of…”
“Knowing me, knowing you”: personalized explanations for a music recommender system
Millecamp, M., Conati, C., & Verbert, K. (2022) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-021-09304-9 · Cited by 64 · see citing works
“To implement such a personalized system, Paramythis et al. (2010) proposed a blueprint with five different layers collecting input, interpreting input data, modeling the current state of the world, deciding which adaptation to apply, and applying that adaptation.”
User Experience Evaluation in Intelligent Environments: A Comprehensive Framework
Ntoa, S., Margetis, G., Antona, M., & Stephanidis, C. (2021) · Technologies · doi:10.3390/technologies9020041 · Cited by 59 · see citing works
Epp, C. D., & Bull, S. (2015) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2015.2411604 · Cited by 55 · see citing works
“Uncertainty can be introduced from a variety of sources or during any level of information processing [66].”
BackgroundThe evaluation of adaptive and personalised information retrieval systems: a review
Mulwa, C., Lawless, S., Sharp, M., & Wade, V. P. (2011) · International Journal of Knowledge and Web Intelligence · doi:10.1504/ijkwi.2011.044120 · Cited by 53 · see citing works
“…design, prototypes Preliminary Early prototype evaluations, evaluation before implementation Van Velsen et al., 2008; Gena, 2005; Masthoff, 2006 Paramythis et al. (2010) have shown how the traditional layered evaluation methods need to be tailored to suit the particular requirements of…”
BackgroundMethodInfluentialvan Velsen, L., Broekhuis, M., Jansen-Kosterink, S. M., & den Akker, H. O. (2019) · Journal of Medical Internet Research · doi:10.2196/11759 · Cited by 52 · see citing works
Inferring user interests in microblogging social networks: a survey
Piao, G., & Breslin, J. G. (2018) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-018-9207-8 · Cited by 49 · see citing works
“Many previous studies on the evaluation of adaptive systems suggested that it is important to evaluate different blocks separately in order to identify the problems in the adaptive systems (Brusilovsky et al. 2001; Paramythis et al. 2010).”
BackgroundAdapting Recommendation Diversity to Openness to Experience: A Study of Human Behaviour
Tintarev, N., Dennis, M., & Masthoff, J. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-38844-6_16 · Cited by 48 · see citing works
“Building on previous studies, which found that friends of a user consistently provided better recommendations than a recommender system [22], this experiment adopts the User-as-Wizard method [23, 24].”
MethodSelf-citationBartling, M., Resch, B., Reichenbacher, T., Havas, C. R., Robinson, A. C., Fabrikant, S. I., & Blaschke, T. (2022) · Cartography and Geographic Information Science · doi:10.1080/15230406.2021.2015720 · Cited by 42 · see citing works
“In the literature, these are also often referred to as the semantic, syntactic, and lexical levels according to the three levels of interaction in an interface (e.g. Stephanidis et al. (2012), Paramythis et al. (2010)).”
BackgroundFrom perception to action using observed actions to learn gestures
Fuhl, W. (2020) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-020-09275-3 · Cited by 41 · see citing works
“The last experiment evaluates the online usage Paramythis et al. (2010).”
BackgroundLearning management system and course influences on student actions and learning experiences
Epp, C. D., Phirangee, K., Hewitt, J., & Perfetti, C. A. (2020) · Educational Technology Research and Development · doi:10.1007/s11423-020-09821-1 · Cited by 43 · see citing works
Mavrikis, M., Gutierrez-Santos, S., Geraniou, E., & Noss, R. (2013) · Personal and Ubiquitous Computing · doi:10.1007/s00779-012-0524-3 · Cited by 38 · see citing works
Tailoring coaching strategies to users’ motivation in a multi-agent health coaching application
Beinema, T. C., den Akker, H. O., van Velsen, L., & Hermens, H. J. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106787 · Cited by 38 · see citing works
“As Paramythis et al. (2010) describe, each layer of an interactive adaptive system should carefully be designed and evaluated.”
BackgroundCena, F., Likavec, S., & Rapp, A. (2018) · Information Systems Frontiers · doi:10.1007/s10796-017-9818-3 · Cited by 37 · see citing works
“Within the RWUM framework, there is a need to mix qualitative with quantitative assessments in a layered , where it is assumed that she is within a location until a sensor in another area is triggeredevaluation approach (Paramythis et al. 2010) even more than in a traditional UM context.”
BackgroundThe quest for validated personality trait stories
Dennis, M., Masthoff, J., & Mellish, C. (2012) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2166966.2167016 · Cited by 36 · see citing works
“To design an intelligent system which adapts to personality, we can utilize the User as Wizard method [9].”
MethodSelf-citationImpact of data collection on interpretation and evaluation of student models
Pelánek, R., Řihák, J., & Papoušek, J. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883868 · Cited by 32 · see citing works
“Proposal for layered evaluation of adaptive systems [32] includes evaluation of data collection, but does not address specifically its impact on subsequent steps.”
BackgroundAugmented Reality For Archaeological Environments On Mobile Devices: A Novel Open Framework
Deliyannis, I., & Papaioannou, G. (2014) · Mediterranean Archaeology & Archaeometry. International Journal/Mediterranean archaeology and archaeometry. International scientific journal · doi:10.5281/zenodo.13699 · Cited by 28 · see citing works
El‐Nasr, M. S., Durga, S., Shiyko, M. P., & Sceppa, C. (2015) · Entertainment Computing · doi:10.1016/j.entcom.2015.07.002 · Cited by 27 · see citing works
“, [11,31,36], and software evaluation [12,27,59,60,66]).”
Assessing the quality of mobile graphical user interfaces using multi-objective optimization
Soui, M., Chouchane, M., Mkaouer, M. W., Kessentini, M., & Ghédira, K. (2019) · Soft Computing · doi:10.1007/s00500-019-04391-8 · Cited by 26 · see citing works
“In this context, several research initiatives have been proposed tomodel the context of use, to design and generate the mobile user interface (Gena and Weibelzahl 2007; Paramythis et al. 2010).”
BackgroundMethodInvolving Users to Improve the Collaborative Logical Framework
Santos, O. C., & Boticario, J. G. (2014) · The Scientific World Journal · doi:10.1155/2014/893525 · Cited by 25 · see citing works
“It defines five layers, corresponding to the main stages of adaptation: (1) collection of input data, (2) interpretation of the collected data, (3) modeling of the current state of the world, (4) deciding upon adaptation, and (5) applying adaptation.”
BackgroundSSLE: A framework for evaluating the “Filter Bubble” effect on the news aggregator and recommenders
Han, H., Wang, C., Zhao, Y., Shu, M., Wang, W., & Min, Y. (2022) · World Wide Web · doi:10.1007/s11280-022-01031-4 · Cited by 31 · see citing works
The details matter: methodological nuances in the evaluation of student models
Pelánek, R. (2018) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-018-9204-y · Cited by 24 · see citing works
“A proposal for layered evaluation of adaptive systems (Paramythis et al. 2010) includes the evaluation of data collection but does not address specifically its impact on subsequent steps.”
BackgroundMethodInfluentialLayered Evaluation of Multi-Criteria Collaborative Filtering for Scientific Paper Recommendation
Manouselis, N., & Verbert, K. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.285 · Cited by 24 · see citing works
“[15] further elaborated their decomposition by proposing five layers (or modules): (i) interaction monitoring, (ii) interpretation and interface, (iii) modeling, (iv) personalization decision making, and (v) applying personalizations.”
InfluentialDesigning emotional support messages tailored to stressors
Kindness, P., Masthoff, J., & Mellish, C. (2016) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2016.07.010 · Cited by 24 · see citing works
“This approach is referred to as the ‘User as Wizard’ method (Masthoff, 2006; Paramythis et al., 2010).2 To simply provide a short sentence stating “John is a CFR and is feeling lonely” however is unlikely to elicit a strong enough feeling within participants so that they would empathize with John…”
MethodSelf-citationGenerating Consensus Explanations for Group Recommendations
Najafian, S., & Tintarev, N. (2018) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3213586.3225231 · Cited by 23 · see citing works
“We used the layered evaluation proposed by Paramythis et al.[7], which suggest that for effective adaptation, the process needs to be decomposed and evaluated in layers.”
MethodThe evaluation of a social adaptive website for cultural events
Cristina Gena, A., Cena, F., Vernero, F., & Grillo, P. (2012) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-012-9129-9 · Cited by 23 · see citing works
“Finally, in Paramythis et al. [2010], the authors identify the following five main layers of adaptation: i) collection of input data; ii) interpretation of the collected data; iii) modeling of the current state of the “world”; iv) deciding upon adaptation; and v) applying (or instan-tiating)…”
BackgroundEvaluation of an adaptive practice system for learning geography facts
Papoušek, J., Stanislav, V., & Pelánek, R. (2016) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/2883851.2883884 · Cited by 23 · see citing works
“Layered evaluation [20, 1] has been proposed as a basic framework for evaluation of adaptive systems.”
BackgroundThe PDA-LPA design space for user interface adaptation
Bouzit, S., Calvary, G., Coutaz, J., Chêne, D., Petit, E., & Vanderdonckt, J. M. (2017) · International Conference on Research Challenges in Information Science (RCIS) · doi:10.1109/rcis.2017.7956559 · Cited by 23 · see citing works
Hauptmann, H., Leipold, N., Madenach, M., Wintergerst, M., Lurz, M., Groh, G., Böhm, M., Gedrich, K., & Krcmar, H. (2021) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-021-09301-y · Cited by 24 · see citing works
Biosignals meet Adaptive Systems
Schultz, T., & Maedche, A. (2023) · SN Applied Sciences · doi:10.1007/s42452-023-05412-w · Cited by 22 · see citing works
Tutorial on Personalization for Behaviour Change
Masthoff, J., & Vassileva, J. (2015) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/2678025.2716264 · Cited by 22 · see citing works
“…and rewards [35,38,53], personalized prompts [28], personalized arguments [29,54-56], tailored choice of techniques [46, 57-60], adapting to culture [61,62], personalized / tailored persuasion for healthy eating [32, 63-65], ethics of persuasive technology [66], and evaluation methods [67,68].”
BackgroundSelf-citationA Framework for Facilitating Self-Regulation in Responsive Open Learning EnvironmentsPreprint
Nussbaumer, A., Kravcík, M., Renzel, D., Klamma, R., Berthold, M., & Albert, D. (2014) · arXiv.org · doi:10.48550/arxiv.1407.5891 · Cited by 34 · see citing works
“llenges of the ROLE framework and its implementation. For our distributed infrastructure and the diverse test beds we adjusted the postulates of the Layered Evaluation Framework for adaptive systems (Paramythis et al., 2010). We applied a multi-method evaluation approach, meaning that different evaluation instruments…”
MethodThe Impact of Adapting Content for Students with Individual Differences
Flores, R., Ari, F., Inan, F. A., & Arslan‐Ari, I. (2012) · Cited by 21 · see citing works
Perceived Level of Usability as an Evaluation Metric in Adaptive E-learning
Alghabban, W. G., & Hendley, R. (2022) · SN Computer Science · doi:10.1007/s42979-022-01138-5 · Cited by 18 · see citing works
“Learning gain Learning effectiveness is a fundamental factor that should be measured when evaluating adaptive e-learning systems [53].”
BackgroundGeneric adaptation framework for unifying adaptive web-based systems
Knutov, E. (2012) · TU/e Research Portal · doi:10.6100/ir732111 · Cited by 18 · see citing works
“Described in [106] the layered evaluation provides a description of the system functionality and helps to solve many related problems.”
BackgroundDigital Nudging for More Ecological Supermarket Purchases
Fechner, W., & Herder, E. (2021) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3450614.3464620 · Cited by 17 · see citing works
“The background knowledge for adaptation decisions is usually derived from implicit interaction cues [15].”
BackgroundSmith, K. A., Dennis, M., Masthoff, J., & Tintarev, N. (2019) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-019-09219-6 · Cited by 16 · see citing works
Self-citationHerder, E., & Zhang, B. (2019) · doi:10.1145/3345002.3349285 · Cited by 15 · see citing works
“Paramythis et al [13] proposed the concept of layered evaluation of adaptation, with several evaluation criteria regarding the quality, appropriateness and necessity with respect to the collection of input data, the interpretation of the data, the way it is represented in a user model, how it is used for deciding upon…”
BackgroundA classification and comparison of usability evaluation methods for interactive adaptive systems
Dhouib, A., Trabelsi, A., Kolski, C., & Néji, M. (2016) · Conference on Human System Interactions · doi:10.1109/hsi.2016.7529639 · Cited by 15 · see citing works
“[17] the evaluation methods can be assigned to one or more of the following five evaluation layers:”
BackgroundMethodInfluentialPersonalization in Public Transport Passenger Information Systems: A Systematic Review and Framework
Vredenborg, M., van Kasteren, A., & Masthoff, J. (2025) · ACM Computing Surveys · doi:10.1145/3721478 · Cited by 14 · see citing works
“Adaptive systems dynamically adjust to the user and/or their context over time [125].”
InfluentialSelf-citationEffects of Individual Differences in Working Memory on Plan Presentational Choices
Tintarev, N., & Masthoff, J. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01793 · Cited by 14 · see citing works
Self-citationA Virtual Selling Agent Which Is Persuasive and Adaptive
Delecroix, F., Maxime Morge, A., & Routier, J.-C. (2012) · Law, governance and technology series · doi:10.1007/978-94-007-5583-3_37 · Cited by 14 · see citing works
Pelánek, R., Effenberger, T., & Jarušek, P. (2024) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-024-09396-z · Cited by 19 · see citing works
“As in the previous layer, however, it might be dif-ficult for the experts to assess only the presentation decisions, if they do not think the list of recommendations is appropriate (Paramythis et al. 2010).”
InfluentialAdaptive systems as enablers of feedback in English language learning game-based environments
Gavriushenko, M., Karilainen, L., & Kankaanranta, M. (2015) · IEEE Frontiers in Education Conference (FIE) · doi:10.1109/fie.2015.7344107 · Cited by 13 · see citing works
Learning adaptivity in support of flipped learning: An ontological problem‐solving approach
Chi, Y., Chen, T., & Hung, C. (2017) · Expert Systems · doi:10.1111/exsy.12246 · Cited by 11 · see citing works
“However, the lack of standards among AH designs has hindered the adoption of AH systems (Paramythis, Weibelzahl, & Masthoff, 2010).”
BackgroundInspection Mechanisms for Community-based Content Discovery in Microblogs.
Tintarev, N., Kang, B., Höllerer, T., & O’Donovan, J. (2015) · Cited by 10 · see citing works
What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs
Kang, B., Tintarev, N., Höllerer, T., & O’Donovan, J. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-47874-6_20 · Cited by 10 · see citing works
“We used a layered evaluation approach [18], focusing on the decision of an adaptation and how it was applied (in contrast to which data was collected or how it was analyzed).”
MethodEvaluating intelligent knowledge systems: experiences with a user-adaptive assistant agent
Berry, P., Donneau-Golencer, T., Duong, K., Gervasio, M., Peintner, B., & Yorke-Smith, N. (2017) · Knowledge and Information Systems · doi:10.1007/s10115-016-1011-3 · Cited by 17 · see citing works
“The methodological approach of the CALO Test was a kind of layered evaluation [14,64].”
MethodDhouib, A., Trabelsi, A., Kolski, C., & Néji, M. (2016) · International Conference on Research Challenges in Information Science (RCIS) · doi:10.1109/rcis.2016.7549289 · Cited by 9 · see citing works
“The factors considered in the decision making process, are related to: temporal, financial and human resources, location, evaluation phases, type of IAS, types of adaptation, adaptation layers, complexity of task, and user characteristics (i.e. number, expertise with the IAS and age).”
BackgroundInfluentialA systematic mapping on adaptive recommender approaches for ubiquitous environments
Machado, G. M., Maran, V., Dornelles, L. P., Gasparini, I., Thom, L. H., & de Oliveira, J. P. M. (2017) · Computing · doi:10.1007/s00607-017-0572-7 · Cited by 9 · see citing works
“Authors could collect some strategies of already known frameworks of recommender system evaluation [59], as well as from adaptive systems evaluation [60].”
BackgroundCognitive personalization for online microtask labor platforms: A systematic literature review
Paulino, D., Correia, A., Barroso, J., & Paredes, H. (2023) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-023-09383-w · Cited by 11 · see citing works
“Furthermore, interactive adaptive systems have formative methods from which can be analyzed the digital work settings (Paramythis et al. 2010).”
Layered Evaluation in Recommender Systems: A Retrospective Assessment
Manouselis, N., Karagiannidis, C., & Sampson, D. G. (2014) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.20368/1971-8829/906 · Cited by 8 · see citing works
“Paramythis et al. (2010) further elaborated their decomposition by proposing five layers (or modules): (i) interaction monitoring, (ii) interpretation and interface, (iii) modelling, (iv) adaptation decision making, and (v) applying adaptations.”
BackgroundInfluentialAnnota: Towards Enriching Scientific Publications with Semantics and User Annotations
Holub, M., Móro, R., Ševcech, J., Liptak, M., & Bieliková, M. (2014) · D-Lib Magazine · doi:10.1045/november14-holub · Cited by 8 · see citing works
Mulwa, C., Lawless, S., O’Keeffe, I., Keeffe, N. A., Sharp, M., & Wade, V. P. (2012) · International Journal of Technology Enhanced Learning · doi:10.1504/ijtel.2012.048312 · Cited by 8 · see citing works
“Both layers should be evaluated separately in order to effectively interpret the evaluation results (Paramythis et al., 2010).”
BackgroundAdaptive E-learning for Supporting Motivation in the Context of Engineering Science
Bauer, M., Bräuer, C., Schuldt, J., & Krömker, H. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-93882-0_39 · Cited by 7 · see citing works
Knijnenburg, B. P., Anaraky, R. G., Wilkinson, D., Namara, M., He, Y., Cherry, D., & Ash, E. (2021) · Springer eBooks · doi:10.1007/978-3-030-82786-1_16 · Cited by 7 · see citing works
“Finally, to ascertain the quality and validity of the UTP module, it should be evaluated using the “layered evaluation” approach [80], which allows input, processing, and output procedures to be evaluated separately.”
MethodTowards Integrating Human-in-the-loop Control in Proactive Intelligent Personalised Agents
Akbar, A., & Conlan, O. (2024) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3631700.3664903 · Cited by 7 · see citing works
“Furthermore, studies involving real users are fraught with challenges: they are typically costly, time-consuming, and plagued by difficulties in participant recruitment [13].”
BackgroundLayered Evaluation for Data Discovery and Recommendation Systems: An Initial Set of Principles
Manouselis, N., Karagiannidis, C., & Sampson, D. G. (2014) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt.2014.152 · Cited by 7 · see citing works
“grouped together the main approaches and suggested the following main layers of adaptation: (i) collection of input data; (ii) interpretation of the collected data; (iii) modeling the current state of the “world”; (iv) deciding upon adaptation; and (v) applying (or instantiating) adaptation [1].”
BackgroundInfluentialUser Models for Motivational Systems
Grasso, F., Ham, J. R. C., & Masthoff, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-28509-7_31 · Cited by 7 · see citing works
Self-citationTailoring coaching conversations with virtual health coaches
Beinema, T. C. (2021) · doi:10.3990/1.9789036552608 · Cited by 6 · see citing works
Adaptive vs. Fixed Domain Support in the Context of Scripted Collaborative Learning
Καρακώστας, Α., & Demetriadis, S. (2014) · Educational Technology & Society · Cited by 6 · see citing works
An Impact Evaluation Framework of Personalized News Aggregation and Recommendation Systems
Zhao, Y., Wang, C., Han, H., Shu, M., & Wang, W. (2020) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wiiat50758.2020.00136 · Cited by 6 · see citing works
“As a result, the recommendation results are inclined to catering to users’ needs [4].”
BackgroundExploring the Adaptation to Learning Styles: The Case of AdaptiveLesson Module for Moodle
Nakić, J., Sabine Graf, A., & Granić, A. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-39062-3_33 · Cited by 6 · see citing works
“user perception, motivation and satisfaction, complementary approaches to evaluation of adaptive systems are emerging suggesting the using of usability evaluation methods such as usability testing and heuristic evaluation [20; 24].”
MethodKoidl, K., Conlan, O., & Wade, V. (2014) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/2631775.2631798 · Cited by 5 · see citing works
“web personalization and have been highlighted throughout different publications [45][47].”
BackgroundReflections on the Evaluation of Adaptive Learning Technologies
Albert, D., & Steiner, C. (2011) · IEEE International Conference on Technology for Education (T4E) · doi:10.1109/t4e.2011.62 · Cited by 5 · see citing works
“Despite an increasing awareness of the importance of evaluation, current frameworks for evaluating adaptive learning systems and user-adaptive systems, in general, can be considered not yet fully elaborated [3][5].”
BackgroundMethodInfluentialDhouib, A., Trabelsi, A., Kolski, C., & Néji, M. (2017) · Ingénierie des systèmes d information · doi:10.3166/isi.22.4.107-128 · Cited by 5 · see citing works
“In 2010, Paramythis et al. (2010) proposed another layered evaluation framework, suggesting the decomposition of adaptation process into five layers including: (1) collection of input data in which data about user interaction are collected, (2) interpretation of the collected data which validate…”
BackgroundMethodInfluentialGhergulescu, I., Zhao, D., Muntean, G. M., & Muntean, C. H. (2019) · International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP) · doi:10.1109/smap.2019.8864865 · Cited by 5 · see citing works
“[35], this paper evaluates the personalisation process that provides media presentation and remedial content.”
BackgroundLindgren, H., & Weck, S. (2021) · Studies in health technology and informatics · doi:10.3233/shti210614 · Cited by 6 · see citing works
Modeling User Psychological Experience and Case Study in Online E-learning
Wu, X., Liu, M., Zheng, Q., Zhang, Y., & Li, H. (2015) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v10i6.5114 · Cited by 5 · see citing works
“How to model and evaluate user experience taking into account user psychological and cognitive properties is a problem [1, 3, 7].”
BackgroundValidity and Reliability of Student Models for Problem-Solving Activities
Effenberger, T., & Pelánek, R. (2021) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3448139.3448140 · Cited by 5 · see citing works
“When evaluating a student model in isolation, we should provide multiple sources of validity evidence [18, 25] — this is analogical to the common advice in psychometrics.”
BackgroundMethodA Virtual Selling Agent Which Is Proactive and Adaptive
Delecroix, F., Maxime Morge, A., & Routier, J.-C. (2012) · Advances in intelligent and soft computing · doi:10.1007/978-3-642-28786-2_6 · Cited by 5 · see citing works
“As stated in [11], the adaptation requires (in our case) to: collect input data (the customer’s answers), interpret data (the interpretation of the customer’s utterances), model the current state of the world (the update of the beliefs), decide upon adaptation (the selection of the offers) and apply adaptation (the go…”
BackgroundModelling user reactions expressed through graphical widgets in intelligent interactive systems
Cena, F., Cristina Gena, A., Mensa, E., & Vernero, F. (2021) · Behaviour and Information Technology · doi:10.1080/0144929x.2021.1931450 · Cited by 4 · see citing works
“…in the literature, interactive systems are studied from di ff erent perspectives, from performance and usability evaluation (e.g. Norman 2002; Paramythis, Weibelzahl, and Mastho ff 2010; Ras-togi and Singh 2016) to decision making (e.g. Cheung and Lee 2010; Jameson et al. 2014) and…”
BackgroundPersonalized, context-aware communication in multimodal public transport
van Kasteren, A., & Vredenborg, M. (2022) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3503252.3534363 · Cited by 4 · see citing works
“In addition to RQ 5, the evaluation of personalization is challenging and requires a layered evaluation approach [33].”
BackgroundOntology-Driven Automatic Generation of Questions from Competency Models
Sitthisak, O., Gilbert, L. H., & Albert, D. (2013) · Advances in intelligent systems and computing · doi:10.1007/978-3-642-37371-8_18 · Cited by 4 · see citing works
Factors Affecting the Choice of Usability Evaluation Methods for Interactive Adaptive Systems
Dhouib, A., Assila, A., Trabelsi, A., Kolski, C., & Néji, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-05909-5_16 · Cited by 4 · see citing works
Epp, C. D. (2018) · Springer eBooks · doi:10.1007/978-3-642-41981-2_92-1 · Cited by 4 · see citing works
Arana-Llanes, J. Y., Rendon-Miranda, J. C., González, G., & Omar, A.-S. H. (2014) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci.2014.137 · Cited by 4 · see citing works
“Methodology for user-centered evaluation of C evaluation goes hand-in-hand with the H involving users as early as possible in the des it is vital in discovering what and how to interactive system [7].”
MethodEvalCHOICE: A Decision Support Approach for the Usability Evaluation of Interactive Adaptive Systems
Dhouib, A., Trabelsi, A., Kolski, C., & Néji, M. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.086 · Cited by 4 · see citing works
User-centered design for personalization
Velzen, V. L. S. (2010) · doi:10.3990/1.9789036531399 · Cited by 4 · see citing works
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Conversational Evaluation of Personalized Solutions for Adaptive Educational Systems.
Labaj, M., & Bieliková, M. (2013) · Cited by 3 · see citing works
Developing Augmented Reality Applications Using Branded Authoring Environments
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Jingar, M. (2023) · Behaviour and Information Technology · doi:10.1080/0144929x.2023.2196583 · Cited by 3 · see citing works
Human Factors in User Modeling for Intelligent Systems
Pera, M. S., Cena, F., Landoni, M., Musto, C., & Starke, A. D. (2024) · Human-computer interaction series · doi:10.1007/978-3-031-55109-3_1 · Cited by 3 · see citing works
Using Simulated Learners and Simulated Learning Environments within a Special Education Context.
Epp, C. D., & Makos, A. (2013) · AIED Workshops · Cited by 3 · see citing works
Runtime Requirements Monitoring Framework for Adaptive e-Learning Systems
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- Authors
- Paramythis, A., Weibelzahl, S., & Masthoff, J.
- Affiliations
- Johannes Kepler University of Linz (AT)
- National College of Ireland (IE)
- University of Aberdeen (GB)
- Corresponding author
- Paramythis, Alexandros
- Weibelzahl, Stephan
- Masthoff, Judith
- Venue
- User Modeling and User-Adapted Interaction
- Published
- 2010-11-28
- Volume
- 20
- Issue
- 5
- Pages
- 383-453
- ISSN
- 0924-1868 (ISSN-L)
- 1573-1391
- OpenAlex
- W2032468592
- Cited by
- 145
- References
- 201
- Citations by year
- 2026: 5 · 2025: 4 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 18 · 2020: 6 · 2019: 10 · 2018: 7 · 2017: 9 · 2016: 14 · 2015: 10 · 2014: 12 · 2013: 12 · 2012: 17
- FWCI
- 21.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- User Modeling and User-Adapted Interaction (publishedVersion)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Software Engineering Methodologies
- Keywords
- adaptive systems, interactive systems, development life cycle, user modeling
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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