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Toward a web-based multi-criteria decision support system for the layered evaluation of interactive adaptive systems

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Abstract

The choice of suitable evaluation methods for the layered evaluation of Interactive Adaptive Systems (IAS) needs the consideration of different factors, leading to a multi-criteria decision analysis problem. This paper proposes the first step toward a Web-based Multi-Criteria Decision Support System (MCDSS). Our proposal is based on a recent multi-criteria decision method called ELECTRE TRI-B-H (Elimination and Choice Translating Reality) to guide the layered evaluation of IAS. The goal is to support the choice of appropriate evaluation methods for individual layers taking into account constraints of the layered evaluation and the individual layers. The appropriateness of each evaluation method is analyzed for the layered evaluation in general and each layer in particular. The use of ELECTRE TRI-B-H method allows decomposing a decision problem into intermediate sub-problems through a hierarchy model and sorting alternative evaluation methods at different levels of the hierarchy. A case study of an adaptive hypermedia system is presented; in this study, the ELECTRE TRI-B-H method has been applied to select the most suitable evaluation methods for each of its layers taking into account the layered evaluation context. The promising results show the feasibility of the proposed approach, leading to various research perspectives.

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Notes

  1. The decision-maker represents the actor who is being aided.

  2. It is called also ELECTRE TRI method.

  3. In contextual design method, users are observed in their work place in order to discover how they go about their work, and in what environment.

  4. The Wizard-of-Oz method can be applied in association with usability test method in design phase. During this method, a designer plays the role of the system [21].

  5. Ten heuristics are proposed by [37], including (1) visibility of system status, (2) match between system and the real world, (3) user control, (4) consistency and standards, (5) error prevention, (6) recognition rather than recall, (7) flexibility and efficiency of use, (8) aesthetic and minimalist design, (9) help users recognize, diagnose and recover from errors, and (10) help and documentation.

  6. Also named usability testing or user test.

  7. Adhesion is the acronym for “Aide à la Décision Humaine pour I’Evaluation des Systèmes Interactifs et leur validation” (translated by: Human decision-making for the evaluation of interactive systems and their validation).

  8. In the MCDA field, very few hierarchical methods have been proposed to deal with the sorting problems using a hierarchy model of criteria, such as Mutli-criteria Spider-gram Cumulative Surface Area (MCSA score) [40]. However, most of these MCDA hierarchical methods consider the criteria at the same level despite their natural organization. The ELECTRE TRI-B-H represents an effective MCDA method for sorting alternatives following a hierarchical structure of criteria.

  9. Four types of decision problems have been introduced in [47], namely (1) choice (P.α), where the problem is to identify the most desirable solution(s), (2) ranking (P.γ), where the objective is to rank solutions from the best to the worst, (3) sorting (P.β), where the aim is to affect each alternative to one of a pre-defined set of classes, and (4) description (P.δ), where the aim is to describe solutions in terms of their main distinguishing characteristics. In this research paper, we focus on the sorting problem in the field of IAS evaluation. In fact, the goal is to assign each alternative evaluation method to a category representing its appropriateness level for the layered evaluation context in general and the individual layers in particular.

  10. The category improvement value represents a measure of performance used to compare the profile limits and alternatives when they have been affected to ordered categories.

  11. The focus group method can be applied in the specification and design phases. When focus group is applied in the specification phase, the participants are told what types of input the target layer would have and they are asked to discuss how a layer should produce its output. In the design phase, participants are shown the layer’s input and output data. In this way, different evaluations are possible for focus group on “specification phase,” “design phase,” and “layer’s output data” criteria. For this reason, we propose to substitute this method with two versions: focus group specification, and focus group design. The focus group specification has a performance of “produced” for the “layer’s output data” criterion and a performance of “applicable” for “specification phase” criterion. Regarding focus group design, it has a performance of “given” for “layer’s output data” criterion and a performance of “applicable” for “design phase” criterion.

  12. The scale can be nominal, ordinal, ratio, interval or cardinal according to Stevens’ typology of data [50].

  13. We recall that these layers refer to the ones identified by Paramythis et al. [43] (ID layer: Interpretation of the collected Data, MW: Modeling the current state of the “World,” DA: Deciding upon Adaptation, AA: Applying Adaptation). More details can be found in Sect. 2.1.

  14. The main goal of AHP is to rank a finite number of alternatives in terms of a finite set of criteria. The interested reader can find more details about the this MCDA method in [48].

  15. It is essential to note that the Knowledge-Based Management Subsystem (KBMS) is an optional component. In the case of this research, the KBMS is not developed and it will be considered as a research perspective.

  16. Two variants in using our proposed web-based MCDSS are possible. The first one is to use it without registration. In this case, the preliminary results are not stored. The application of this variant is preferable for small evaluation projects with a short list of alternatives (evaluation methods and evaluation sub-attributes) and decision criteria. The second variant consists of creating a DM account. In this case, a storage of the preliminary results is allowed.

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Acknowledgements

We would like to thank the anonymous reviewers for their careful reading of our manuscript and their insightful comments and suggestions.

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Authors and Affiliations

  1. Miracl, Faculty of Economics and Management Sciences, University of Sfax, Sfax, Tunisia

    Amira Dhouib & Mahmoud Neji

  2. LAMIH-UMR CNRS 8201, Univ. Polytechnique Hauts-de-France, Valenciennes, France

    Christophe Kolski

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Correspondence to Amira Dhouib.

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Appendices

Appendix A

The defined rules for the intermediate criteria related to layers (layer 2, layer 3, layer 4, and layer 5) criteria

For layer 2, two rules are defined. These rules have been defined based on the evaluation settings of layer 2 with produced input data, given output data, medium temporal resources, and high budget.

  • Rule 2.1 If (evaluation-attribute-L2 = moderately covered L2 or highly covered L2) and (layer 2’ input data = produced) and (layer 2’ output data = given) and (L2 temporal resources = medium or low), then Layer 2 = L2 suitable evaluation methods

  • Rule 2.2 If (evaluation-attribute-L2 = moderately covered L2 or not covered L2) and ((layer 2’ input data = given) or (layer 2’ output data = produced) or (L2 temporal resources = high)), then Layer 2 = L2 unsuitable evaluation methods

For layer 3, two rules are defined. These rules have been defined based on the evaluation settings of layer 3 with produced input data, given output data, medium temporal resources, and high budget.

  • Rule 3.1 If (evaluation-attribute-L3 = moderately covered L3 or highly covered L3) and (layer 3’ input data = produced) and (layer 3’ output data = given) and (L3 temporal resources = medium or low), then Layer3 = L3 suitable evaluation methods

  • Rule 3.2 If (evaluation-attribute-L3 = moderately covered L3 or not covered L3) and ((layer 3’ input data = given) or (layer 3’ output data = produced) or (L3 temporal resources = high)), then Layer 3 = L3 unsuitable evaluation methods

For layer 4, two rules are defined. These rules have been defined based on the evaluation settings of layer 4 with given input data, given output data, medium temporal resources, and high budget.

  • Rule 4.1 If (evaluation-attribute-L4 = moderately covered L4 or highly covered L4) and (Layer 4’ input data = given) and (layer 4’ output data = given) and (L4 temporal resources = low or medium), then Layer 4 = L4 suitable evaluation methods

  • Rule 4.2 If (evaluation-attribute-L4 = moderately covered L4 or not covered L4) and ((layer 4’ input data = produced) or (layer 4’ output data = produced) or (L4 temporal resources = high)), then Layer 4 = L4 unsuitable evaluation methods

For layer 5, two rules are defined. These rules have been defined based on the evaluation settings of layer 5 with given input data, given output data, high temporal resources, and high budget.

  • Rule 5.1 If (evaluation-attribute-L5 = moderately covered L5 or highly covered L5) and (layer 5’ input data = given) and (layer 5’ output data = given), then Layer 5 = L5 suitable evaluation methods

  • Rule 5.2 If (evaluation-attribute-L5 = moderately covered L5 or not covered L5) and ((layer 5’ input data = produced) or (layer 5’ output data = produced), then Layer 5 = L5 unsuitable evaluation methods

Appendix B

Robustness analysis of ELECTRE TRI-B-H method

After the assignment of alternative evaluation methods to their respective categories, attention should be turned to carry out the analysis of robustness. The goal of this analysis is to check in which way the variations introduced in the parameters of the intermediate criteria influence the assignment of alternatives to the categories at the root node. This analysis is essential since the values of these parameters are imprecisely determined. Two scenarios with different configurations of parameters are considered in this study, as shown in Table

Table 13 Robustness configuration parameters
Full size table

13. The analysis will focus on variations in the veto thresholds of intermediate criteria. The other parameters (i.e., preference thresholds, and indifference thresholds) will be left untouched since the scales of the decision criteria in our case consist of few levels (at most three). In the first scenario, the veto threshold is activated if some decision criterion is not fulfilled previously. This means that the assignments at intermediate criteria should fully accomplish the defined rules. This scenario is the one applied to obtain the results presented in Table 12. In the second scenario, the veto is slightly increased and it cannot be applied until a difference of more than two units is obtained.

Figure 

Fig. 9
figure 9

Number of alternative evaluation methods assigned for each category by applying two scenarios

Full size image

9 presents the number of alternatives assigned to each category in the root node after the consideration of two scenarios. From this figure, we can see that the assignment of alternatives to the categories at the root is consistent with the profile limits defined through the rules presented in Table 8.

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Dhouib, A., Kolski, C. & Neji, M. Toward a web-based multi-criteria decision support system for the layered evaluation of interactive adaptive systems. Univ Access Inf Soc 22, 415–443 (2023). https://doi.org/10.1007/s10209-021-00850-y

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