Advisor Systems Research Articles

Improving the Status of the Academic Advisor and Mentoring System in order to Meet the Educational Needs: a Case Study in Tabriz University of Medical Sciences in 2021

Introduction: Counseling by experienced, interested and motivated professors helps students in facing educational problems and achieving educational goals and is considered a necessary action in the educational system. The aim of this study is to improve the status of academic advisor and mentoring system in order to meet the educational needs of students of management and medical informatics faculty of Tabriz University of medical sciences.
 Methods: This is an action research study that was conducted during two semesters of the academic year of 1401-1400. First, a quantitative study was conducted regarding students' satisfaction with the academic advisor system, and then during numerous meetings in the form of focus group discussions with the main owners of the process, academic advisor systems problems and solutions to improve it were identified. In the last stage, after the implementation of interventions and solutions, the degree of improvement of the process was checked.
 Result: The participation rate of students in completing the satisfaction questionnaire was 65%. The degree of students' satisfaction with the professor's performance before the process improvement was 42.4%. After identifying the main problems and challenges of the academic advisor and mentoring system (4 main themes and 7 sub-themes), actions aimed at increasing students' awareness, clarifying the process of the academic advisor and mentoring system, increasing the motivation of faculty members to cooperate in the academic advisor system and satisfaction survey of students was conducted in order to improve the process and adjust the problems of this system. With the implementation of the proposed interventions, the overall satisfaction level increased to 66.7%.
 Conclusion: The promotion of this process increased the satisfaction of students, improved interactions between students and professors, and reduced the distance between the educational environment and students during the epidemic of Covid-19.

Decline, adopt or compromise? A dual hurdle model for advice utilization

Research on advice utilization often operationalizes the construct via Judge Advisor Systems (JAS), where a judge’s belief is elicited, they are provided advice, and given an opportunity to revise their belief. Belief change, or weight of advice (WOA), is measured as the shift in the judge’s belief proportional to the difference between their original belief and the advice. Several JAS studies have found WOA typically takes on a trimodal distribution, with inflation at the boundary values of 0 (indicating a judge declined advice) and 1 (adoption of advice). A dual hurdle beta model is proposed to account for these inflations. In addition to being an innovative computational model to address this methodological challenge, it also serves as a descriptive theoretical model which posits that the decision process happens in two stages: an initial discrete “choosing” stage, where the judge opts to either decline, adopt, or compromise with advice; and a subsequent continuous “averaging” stage, which occurs only if the judge opts to compromise. The approach was assessed via reanalysis of three recent JAS studies reflective of popular topics in the literature, such as algorithmic advice utilization, egocentric discounting effects, and judgmental forecasting. In each case new results were uncovered about how different correlates of advice utilization influence the decision process at either or both of the discrete and continuous stages, often in quite different ways, providing support for the descriptive theoretical model. A Bayesian graphical analysis framework is provided that can be applied to future research on advice utilization.

Smart Control Mechanisms in Digital Technologies of Decision-Making

This paper considers the problems of adopting digital technologies in decision-making. Two types of digital decision technologies are described, namely, the direct (or traditional) technology with human decision-making based on computer-aided advisor systems and also the inverse technology with independent computer decision-making in which a human merely monitors the decision process. These technologies are compared with each other, and some applications are discussed.

ISABELA – A Socially-Aware Human-in-the-Loop Advisor System

Currently, we are surrounded by all sorts of smart devices. Nevertheless, surprisingly, apart from e-health systems, few applications consider humans as active system players, as most applications/systems have the sole objective of providing some service to humans without considering their intents, actions, emotions, or social context. In this respect, they can be looked at as open loop, rather than integrating humans in a feedback loop, with potential to react to and change the environment. Systems that do consider human feedback are called Human-in-the-Loop Cyber-Physical-Systems (HiTLCPS), and these are now looked at as essential components of intelligent advisor systems (AS), i.e., systems that can take decisions and provide advice based the user environment and mood. In this paper, we present a HITLCPS AS system that combines online social networks (OSN) data with a variety of other data, such as environmental data and personal mobile devices data, in order to provide user advice. The paper details the system architecture, presents a data analysis from the collected dataset, provides results for some of the built models, and discusses future research directions.

FanOnTour: A Novel Approach to Travel Advisor Systems

FanOnTour: A Novel Approach to Travel Advisor Systems

Improving the Delphi process: Lessons from social psychological research

The Delphi technique was largely developed to avoid the problems of freely interacting groups such as dominant individuals and pressure to conform to the majority view. Our review of the social psychological literature reveals some obstacles to Delphi achieving its full potential relative to other cheaper and easier methods of aggregating judgment. We identify residual normative and informational pressures towards consensus that potentially reduce process gain that might otherwise be achieved. For instance, panelist confidence may act as a signal of status rather than be a valid cue to expertise, whereas consensus appears to have a strong influence on the final outcome that can reduce its accuracy when there are valid minority opinions. We argue that process gain in Delphi must occur through those further from the "truth" changing their opinion more than those closer to the truth, with the general direction of opinion change being towards the truth. For such virtuous opinion change to occur we suggest the need to both facilitate opinion change and provide good cues as to where the truth lies. Research on Judge Advisor Systems shows that people usually do not change their opinion as much as they should, giving too much weight to their own opinion and too little to the views of others—this bias can be reduced by increasing involvement and motivation. In addition, we propose that the best way to provide good cues as to the direction of the truth is to elicit rich reasoning from panelists about the judgment or choice in question, then use this as feedback. We suggest practical ways of focusing and deepening panelists’ consideration and evaluation of such reasoning—such that all proffered opinions are well-evaluated. Additionally, we propose a model of opinion change in Delphi for use as a paradigm for future process-orientated research.

A competency—driven advisor system for multi-actor learning environments

Advising plays a central role in technology-enhanced learning (TEL). Based on a diagnosis of the activity of a learner and on relevant knowledge of this learner, an advisor system compiles and delivers some useful advices or explanations. Since 1994, we have been working on advisor systems. This paper presents the latest work on a multi-agent system that gives advice on tasks and resources based on competency-driven user models and on ontology-based multi-actor learnflows.

A manufacturability advisor for spun and rollformed sheet metal components

In today’s manufacturing scenario, accounting for manufacturability considerations at the product design stage is not an option but a necessity. When CAD/CAM tools are used to reduce design lead times, accounting for manufacturing process related considerations implicitly is often difficult. Availability of manufacturability advisor systems that analyze part geometry and other product related information for ease of manufacturing could be a solution to this problem. The purpose of a manufacturability assessment is to give feedback to the designer in order to make the product/process more effective. Present research is concerned with the development of an automated manufacturability advisor for sheet metal components. Unlike most of the work done in the past that concentrated on bending and other forming processes, present work deals with sheet metal components manufactured by two different processes, namely “spinning” and “rollforming”. Effectiveness of the proposed manufacturability advisor is demonstrated by taking the number of industrial parts as examples.

Contribution à l’ingénierie des systèmes conseillers : une approche méthodologique fondée sur l’analyse du modèle de la tâche

An advisor system is a system that proposes a just-in-time first-line help to the users of a given software, on the basis of an analysis of the actions and productions of the user. Within the Licef laboratory, two different types of advisor systems are developed : advisor systems dedicated to students using the web-based interactive learning environments developed with Explor@ and advisor agents dedicated to courseware designers using the course design methodology MISA and its internet-based workbench ADISA. The objective of the work presented in this paper is to elaborate a common methodological framework facilitating the design of these systems. The work presented here focuses on the identification of the pieces of advice ; we propose an approach based on the analysis of a model of the considered task.

Information Sampling and Confidence Within Groups and Judge Advisor Systems

This study compared how the judge advisor system (JAS) and unstructured groups discuss common and unique information. Three differences between the two were measured for their effect on communication of information. These were responsibility for decision, consensus seeking, and equity of participation. Participants were given information about two drugs and had to decide which of the two to market. Half met in unstructured 3-person groups and made the decision as a group. The rest met in JASs whereby the judge discussed the information with each advisor separately and then made the decision individually. Advisors mentioned but did not repeat a higher proportion of unique information than group members. Judges felt more responsible for, reported putting more effort toward, and had higher confidence in the decision than did group members. There was more inequity of participation and consensus seeking in JASs compared to groups. Differences are discussed in light of results found on information exchange.

Psychopharmacological advisor systems

Psychopharmacological advisor systems

Un mécanisme de production de conseils exploitant les relations de composition et de précédence dans un arbre de tâches

ANG:advisor systems, intelligent tutorial systems, task graph, part-whole relation, temporal precedence relation, trace analysis, plan recognition.

Turorial design for an advisor system

An advisor system initiates the giving of advice to the user of a computer-based system when such advice is deemed to be desirable. This assistance function must be aligned with more direct tutorial provision of an introductory nature, when the latter is provided. This leads to a number of design issues, many of which are explored here in the context of the early design of an advisor system for data modelling. These tutorial considerations apply broadly to advisor systems as a whole.

Intelligent Real-Time Process Monitoring

Real-time monitoring systems are to provide information within a limited response time about the operation of a time variant system that has a complex model. For the sake of preventing the information overload in the operators' room a need for selective, feature extracted information displaying, monitoring, alarming system has arised, A straightforward way to increase the intelligence level is to integrate advisor systems and other artificial intelligence techniques into the monitoring system. Advisor systems having the structure of the wide-spread used rule-based expert systems can not handle so complicatea a task, or sometimes can handle it with considerable restriction.