situational predictors: applying user’s sense making
behavior to adaptivity
Patricia C. Nsc. Souto PhD Student
Information, Technology and Society Research Group Department of Information Science
Loughborough University, Loughborough, Leicestershire, LE11 3TU
P.C.Nascimento-Souto@lboro.ac.uk
Abstract. Information about the user which supports the adaptation of information (adaptive user model used in adaptive systems) is the key element that enables an effective adaptation decision and result. However, current approaches have been based solely on human dimensions that exclude factors and events which drive and influence the individual information seeking and use behaviors. The current information needs predictors used to adapt information to a user is not sufficient to explain differences on information needs, seeking and use. Consequently, the adaptation of information is impacted, since the results may not reflect what a user needs in a situation, in his/her information space and context. The purpose of this research is to include the characteristics of user’s problematic or knowledge gap situations in the information that supports adaptation. In order to do this, the characteristics of individual’s sense making processes will be applied as predictors of information needs and behaviors and as the drivers of information adaptation in adaptive systems, such as scientific digital libraries. This proposal changes the focus of the adaptation from being based on user’s characteristics to be based on the characteristics of information need, seeking and use situation. This research applies a constructivist approach to adaptive user model and the expected results are a situational adaptive methodology and user model that support an adaptation as close as possible to the complex nature of human information needs and behavior, helping individuals in his/her information acquisition.
1 Introduction
Considering that the core value of adapting information1 to a user is the closest match between the information accessed and the user’s information need, the design of the adaptation should considerate all aspects surrounding this information need and how it is attended by the user. Thus, in order to adapt to what matters to users, adaptation strategies and decisions should consider what, where, how and why this information need emerges and how it is translated, and equally, they should consider how the user seeks2 and use information to attend this need.
The emergence of an information need is influenced by the user’s perception and translation of his/her information need, by how the user perceives the situation that caused the information need and by the way he/she performs information seeking and use. Frenette (1999) has pointed out that “humans only use information resources in the context of their own experience – where they come from, what they are struggling with and where they are going”.
Thus, information use is situational, contextual and it is influenced by users’ internal and external resources and it does not occur independently the situation or the problematic situation in which the information use ocurs. Within this context, users have different ways of seeking, obtaining information and judging relevance of information in varied moments. Identical users may differ in seeking and use information behaviors for the same or for different tasks and goals. A single user may also differ their information seeking and use behaviors, either
1
The “adaptation of information” is related to adaptive systems and to adaptive information access, which functions are to help and support people in finding information (searching, browsing and visualization).
2
Information Seeking Behavior is defined as “the purposive seeking for information as a consequence of a need to satisfy some goal. In the course of seeking, the individual may interact with manual information systems (such as a newspaper or a library), or with computer- based systems (such as the World Wide Web)” (Wilson, 2000).
for a single task or for different tasks in different times. The information behavior3 (seeking and use) also differs within and between groups (i.e. to pertain to a professional class does not indicate that two engineers will seek and use information in the same way for a given task), within and between domains (i.e. the fact that two different people are researching the same domain does not indicate that they will seek and use information in the same way for a given task), and related to tasks (i.e. two people with identical tasks does not indicate that they will seek and use information in the same way).
Thus, the adaptation of information to a user’s information needs demands a clear understanding about how the user seeks and uses information and what explains the differences and variances in these behaviors. The behavioral ‘explanatory’ dimensions of the differences in information seeking and use may be identified, modeled and used to effectively drive the adaptation, to help increasing the predictability power of the system and the suitability of adaptation decisions and results.
The adaptation process is directly related to adaptive systems. Adaptive systems adapt its behavior to individual users based on information about him/her (it is implicitly collected during the user-system interaction or it is explicitly asked to users) and they perform the adaptation using some form of learning, inference or decision making (Jameson, 2003). To be adaptive, the system needs to have enough intelligence to recognize users’ differences in seeking and using information. Based on this knowledge, the system infers and identifies the correct procedure to help the user on his/her efforts to solve an information problem. One way to give this intelligence to the system is a knowledge base about the user, the so called the user model (hereafter UM) (Fig 1).
Data about user
User Model System
Adaptation effect
Adaptation User Modeling Collec
ts
Processes
Pro cesses
Data about user
User Model System
Adaptation effect
Adaptation User Modeling Collec
ts
Processes
Pro cesses
Figure 1 – Classic “user modelling – adaptation” in adaptive systems (Brusilovsky, 1996)
A key element in the user-adaptivity is the information about the user, which determines and supports the system learning and inferences and subsequently, drives the adaptation of information to the user’s needs. According to Torre (2000), the identification of the correct information that attends to the user’s needs has been a great challenge in adaptive systems.
In the context of adaptive information access, the provision of the most suitable combination of information to a user (documents, articles, papers, summaries, paragraphs, graphics etc.) that intelligently attend user’s information needs is the core added value that an adaptive system should create. And from a user’s viewpoint, the ‘most suitable’ information is the one which attend a specific need that he/she has in the moment X, in which he/she should understand/find/know/improve knowledge about something, in order to do or understand another thing.
However, as explained above, adaptation in the context of information access has been focused on an atomistic view of the user, excluding the factors from his/her the information need, information seeking and use behaviors,
3
Information behavior is defined as “Information Behavior is the totality of human behavior in relation to sources and channels of information, including both active and passive information seeking, and information use. Thus, it includes face-to-face communication with others, as well as the passive reception of information as in, for example, watching TV
advertisements, without any intention to act on the information given (Wilson, 2000. Another definition is “the human behavior dealing with generation, communication, use and other activities with information, such as information seeking and behavior and interactive information retrieval” Ingersen & Jarvelin (2005, p. 21).
from the dynamic and interrelated environment in which these behaviors are embedded, from the user’s specific information space and from his/her information problematic situation and context.
This paper explains the proposal of a PhD research related to the appliance of a constructivist and socio- cognitive viewpoint to adaptivity of information. This research proposes the inclusion of dimensions related to the user’s information seeking and use behaviors, applying them to an adaptive-user-model (UM) which aims to drive and support the adaptation of information access.
This paper is structured as follows: second session describes the gaps in the current knowledge related to user modelling for adaptive systems, the third session explain how this research aims to fill this gaps, the sessions 4, 5, and 6 present the research questions, objectives and methodology, respectively.
2 Adaptive User model in adaptive information access
The focus of this research is in the user model used4 with the function of supporting people in their information acquisition process, helping them to find information they need and to access5 it in the most effective manner. According to Jameson (2003), this function comprises the following other functions: (a) helping people to find information, (b) tailoring information presentation, and (c) recommending products. The first function is the focus of this research and it supports browsing, search, filtering, and spontaneous provision of information. Within this function, Brusilovsky & Tasso (2004) have pointed out the four paradigms in adaptive information access: ad-hoc information retrieval, information filtering, hypertext browsing and information visualization. This research will focus in these four paradigms of adaptive information access.
Commonly, the information in user models includes the following dimensions: user’s preferences, goals, knowledge, background, hyperspace experience, interests, individual traits (cognitive factors, personality factors and learning styles); the plans with which the user wants to reach his/her goals; the knowledge (beliefs) of the user about a particular domain; the user’s general knowledge; usage and environment information (user location and platform); attitudes, proficiencies (e.g. task domain knowledge, proficiency with system), user classification or relevant common characteristics and/or behavior of users pertaining to specific user subgroups (stereotype); and finally perceptual and motor skills limitations (Kobsa, 1990, 1993, 2004, Rich, 1983, Brusilovsky, 1996, p.8, 2001, p.96, 2003, p.1, Kules, 2000, Jameson, 2001). More recently, affective, emotional and cognitive dimensions have been considered in the adaptive systems and computing (Hudlicka & Mcneese, 2002, p.9, Picard & Klein, 2002, Berthouze & Lisetti, 2002, pp. 50-51, Lisetti, 1999).
The user models applied for adaptive information access (which is the focus of this research) have mainly adopted the following dimensions of the information about the user which supports adaptation: (a) user’s characteristics (i.e. socio demographic and psychographic) or users groups characteristics (stereotypes), (b) preferences, (c) interests in topics explicitly manifested by users, (d) interests in information inferred mainly by queries, by the words and by the meaning of words present in summaries and snippets of documents accessed and navigated, by the user’s browsing behavior and by interactive actions such as ‘save’, ‘print’ or ‘bookmark’ , (d) domain expertise, (e) level of knowledge and experience related to topics and tasks, (f) user’s relevance perception about a information (by relevance feedback); (g) cognitive and life styles, (h) other’s interests in the information (by social navigation and annotation). To exemplify the information dimensions used in UM which are cited above, some of the adaptive systems applied to information access are outlined below.
Psarras & Jose (2006) designed and developed an adaptive information retrieval system, called PIA (Personal Information Assistant), which adapts documents delivery to the multiple and evolving search interests of users. The user model is composed of user’s interests, inferred by the keywords used in search iterations. These keywords have weights assigned to them and they are derived by extracting terms from the documents, snippets and summaries of documents recently viewed.
The SiteIF system (Magnini & Strapparava, 2004, p. 239) proposes relevant documents based on the user’s interests, which are identified by the meanings of the words present in the documents in which the user navigates. The system learns the users’ interests by considering users’ browsing activities (requested pages) and predicts the documents that will be interesting to the user. During a user’s browsing in a navigation session, the system creates the user model “as a semantic network whose nodes represent senses (not just words) of the documents requested by the user”. Thus, the user model in this system is based on users’ interests, which are
4
User models have been applied in learning, collaboration, information access and TV programming. 5
identified by the semantic representation of the browsed information (i.e. news), rather than words. This user model drives the retrieval of new documents, with semantic relevance.
Also related to news and their adaptation to users, Ardissono, et al. (2001) have explained SeAN (Server for Adaptive News), which is an adaptive system that enables personalized access to news servers on the Web. Using a user model based on stereotypes, SeAN decides the sections and the news to be delivered to the user, the level of detail that each new should be presented and also the advertisements that should be shown. The user model is initiated with stereotypes and updated by tracking the user’s interaction while browsing the news. The user’s characteristics and interests which compose the user model are the following: interests (user’s interests on topics of news), domain expertise (expertise of user on the topics), cognitive characteristics (cognitive capabilities), life styles (psychographic and socio-demographic).
Gauch et al. (2003) discussed the ‘Obiwan’ system, which uses ontologies to provide personalized information access. The user model is automatically and implicitly created, also based on ontology. It is compound of the user’s interests by analyzing the user’s browsing behavior (content, length, and time spent on each Web page they visit) and no user feedback is used. Each page visited is automatically classified into concepts of the ontology, attributing and accumulating weights to them.
The Knowledge Sea II project, as explained by Brusilovsky et al. (2005), is a system that enables personalized information access (ad-hoc information retrieval, information filtering, hypertext browsing, and information visualization), helping users in their localization of resources according to their goals, knowledge and interests. The approach used is known as social navigation, which is heavily based on feedback of members of a group. The user navigation is eased and supported by traffic-based visual cues and annotation-based cues, expressing the density of traffic in a specific topic (shows the volume of people who are using a resource), and the density of annotations made by users. The retrieval of information is based on page relevance to the query and does not consider the ‘social relevance’ of links resulted by the traffic navigation cues. The search results are calculated on traditional way and only the traffic-based visual cues are shown, but the search results do not adapt to these cues.
Mizzaro & Tasso (2002a, 2002b) implemented the Information Retrieval Assistant (IRA), a system that provides suggestions to users who are searching for papers paper and e-prints in a scholarly publishing portal for physics (i.e. the Torii portal). The user model is built by the monitoring of user’s session history, and any action performed by the user is considered in the building of a session model. The new situations are derived from the user whole sessions history.
Ruvini (2003) has implemented Toogle, which is a front-end to the Google search engine and which adapts the search results according to the user’s browsing behavior over the Google results. As the user go through the proposed list of results from Google, machine learning techniques infer his/her search goals from clickstream data. The user model is compound of user’s search goal which is inferred by the browsing behavior in the list of the search results. This model is used then, to reorder the list of results of pages the user has not visited yet. Diaz et al. (2005) have developed a Web information system that personalizes summaries of content to users, according to the combination of short and long term user models. The former is created by feedback terms acquired from the system-user interaction, and the latter is created by keywords provided by the user.
Hynecos is an adaptive medical information system that provides hospital information to doctors, nurses, patients and students. According to Vassileva (1994) This system uses individual user models based on the user's level of experience related to a specific task, and this user model drives the adaptation of “the style of viewing, the form of presentation, and the screen layout”. This user model is related to a user class model, which is based on profession (e.g. doctor, nurse or patient), location (e.g. ambulance), and ranks within professions (5 stages according to the profession).
Waern (2004) focused on adaptive information filtering based on users’ profiles, generated automatically (based on feedbacks) and/or by direct user involvement. The personal profile or user model describes users’ interests on a list of topics.
Icarelli & Sciarrone (2004) have also developed an information retrieval system that works as an adaptive interface to the search engine Alta Vista TM, and which is comprised of two modules, which are a user modelling system (called HUMOS) and a content-based information filtering (called WIFS). The user modelling system builds and maintains long term individual user models, which are based on stereotypes. The information filtering module selects html/text documents in the field of Computer Science from the Web, based on user’s interests. The information about the user that is considered in this user model is composed of the user’s interests in specific topics within domains. These interests are subjects which are of interest of the user and to which weights are given by the user.
Still within the context of adaptive information retrieval, Mathe & Chen (1996) have developed the researches and developed the Adaptive Hyperman system, which performs adaptivity and adaptability. This system uses a user model based on the so called ‘Adaptive Relevance Feedback’, which supports adaptive information retrieval, presentation and navigation, based on a second-level of indexing. Users assign concepts to information units and the system automatically builds a conceptual network structure, which is used for adaptive information
access. Adaptive HyperMan enables users to annotate and create hyperlinks in the content, which are judged by others users as relevant or not, and thus, updating and adjusting the user model (Rabinowitz et al. 1995). Related to the adaptive information browsing, Liberman (2001, 1997, and 1995)developed Letizia, a system that suggests links and recommends web pages to the user, according to the page he/she is currently browsing, and only according to the current page because “users have many interests over time”. The information about the user that bases the adaptation is compound of user’s interests derived from the recording and analysis of his/her browsing activity in real time. Also developed by MIT, Powerscout (Lieberman et al., 2001) also learn the user’s preferences by observing and monitoring the user’s browsing, and then, provides recommendations in real time, “but making use of a traditional search engine to search the Web in general”.
Also related to the adaptive web browsers, Chan’s (2000) approach to this topic is creating the user model based on user’s browsing and the content of the pages visited by him/her. To identify the user’s level of interest, a metric is used and his/her access patterns are summarized and then, this metric is used to base the ordering of the search results and to base the pages recommendation. The recommendation is also based on collaborative filtering. The sources of information to infer user’s interests are acquired by the bookmark, the history of user’s requests in the sessions, the access logs and the content of pages (links visited).
In the function of adaptive information visualization, Leuski & Allan (2004) and their system Lighthouse, approach the adaptation of information retrieval based on users’ relevance feedback. The user model applied for this adaptation is comprised of the user’s assignments of relevance to documents that were retrieved. The user’s attribution of relevance is given to the retrieved documents and then, this attribution modifies and adapts what the authors denominate as the ‘User Relevance Model’ (URM). The documents considered as relevant are continuously used to adjust the query and reorder the documents by “probability of being relevant to the new query”. Three types of URMs help the adaptation and visualization of retrieval results, performing the following tasks: (a) ranking documents according to their similarity to other documents previously indicated as relevant, (b) clustering them, and (c) creating “accurate visual representation of inter-document similarities” of the retrieved set, by mapping the documents by similarity or color coding and according to their relevancy.
Related to adaptive hypertext, Milosavljevic & Oberlander (1998) presented two systems (PEBA II and ILEX), which automatic adapt hypertext using techniques from natural language generation systems. In ILEX system, the hypertext pages are created dynamically and thus, they are tailored in real time, to a single user in a specific communicative situation6. This adaptation is based on user’s knowledge related to an object (from a museum gallery) and it is also based on the discourse historyor “the objects which the visitor has already seen”6. PEBA-II System is like an intelligent online encyclopedia and the hypertext is constructed based on a simple user model that differentiates between naïve and expert users, and the user model also considers the discourse history (Dale et al., 1998, Milosavljevic et al. 1998).
As explained above, the current approaches to adaptive information access exclude factors that give rise to the user’s information needs, and that largely influence and drive the information seeking and use. These factors include the following: the context of the information need (i.e. the influences of work environment factors, and social-cultural, political and physical contexts); the role of the individual in relation to the task, context and to the performance; the personal, interpersonal and environmental barriers; and psychological, cognitive, affective needs (based on Wilson’s information behavior model, Wilson, 2005, pp. 31-36). The current approaches have also shown a tentative of including user’s context in the user model, which was supposed to be given by the queries history and snippets and summaries accessed and consequent inference of goals. However, the meaning of ‘context’ is not clear in the adaptive systems (Computer Science) literature and it can mean many things, such as the device and technical environment the user is acting.
The dimensions considered in these UM are not qualitatively sufficient to explain the differences and variances between users with the same interest, for example. Why two teenagers who needs to know about health issues may seek and use information completely different from one another? Why people from the stereotype ‘expert in the domain’ seek and use information about quantum physics differently? Even in a domain, or in a group of users (the so called stereotypes), the way each one seeks and use information is different. Cheuk (1999) has pointed out the existence of a more dynamic approach that contrasts with the ‘domain’ approach, whereby “researchers propose that even within a domain, there are differences in people's information seeking and use behaviors at different times, usually construed as in different situations and/or different contexts”.
At the root, the dimensions used in current UM for adaptive information access are derived from a set of concept predominant in Computer Science. The concept of information needs adopted in current UM considers the user
6
as a unidimensional entity, statically across time. The conceptual framework on which this perspective is focused, considers the concept of information as an external entity of the individual, whose behavior and complexity are not considered. This perspective is based on findings about user’s characteristics that are constant across time-space (e.g. the demographics and personality measures) or on characteristics of situations that are also considered as a constant to all these situations (e.g. the domain and task characteristics) (Dervin, 2005). However, predicting information seeking and use based only on the most common information about the user explained above (e.g. socio demographical, psychological and geographical), provides the prediction of differences in information source use (Dervin, 1989, p.56).
Therefore, adaptation of information should attempt to include other dimensions that can explain these differences in informational behaviors. Thus, the information about the user included in an adaptive UM should go beyond user’s characteristics, interests and preferences and consider a more holistic view of the user’s information seeking and use behaviors and the whole set of facts and events surrounding these behaviors. And in the information access, Cole et al. (2005, p. 15) have pointed out that rather that considering that an information need is represented by a query, user-oriented researchers consider that “a more realistic approach is to bringing the user’s problem or problematic-situation into the interaction”.
The limitations in assumptions about user information required for adaptivity have been argued by (Hudlicka & Mcneese, 2002 p.9), who emphasized that the user states (i.e., cognitive, physical, affective and those related to the personality) should also be considered and addressed for user modelling and adaptation. Lisetti (1999, pp. 50-51) has also discussed this topic, as follows:
“Researchers in User-Modelling (UM), Human-Computer Interaction (HCI) and
Artificial Intelligence (AI) need to learn more about the unsuspected strong interface between affect and cognition. Affective states play an important role in many aspects of the activities we find ourselves involved in, not excluding tasks performed in front of a computer”.
Therefore, the prediction of how the information should be adapted to the user, should consider information beyond their characteristics and also include specific user’s situation when a knowledge gap, a problem or an inadequacy in his/her knowledge is faced, which obstructs his/her movement or process of achieving a goal, understanding a topic, solving a problem, creating meaning or completing a task. Since information behavior and needs are not linked solely to the user’s characteristics, but also to the situation and the factors in this situation that have triggered the seeking and use of information, the user models with the purpose of adaptivity should also consider the factors and characteristics involved in these situations and triggers. Thus, information behavior that is linked to situations and to cognitive events, is better predicted when the characteristics of these situations and events are used, instead of the characteristics of the participants (Wilson et al., 2000, p.57). Therefore, the sense making situations, the cognitive events and the descriptors of context, situations and practices can be added in the user model and enable the information about the user to reach high levels of predictability.
3 Research proposal
Considering the context outlined above, this research proposes the inclusion of other dimensions of information in the adaptive UM, which are derived from informational behaviors. These dimensions are derived from a problematic situation perceived by the user, in which he/she faces an inadequacy of knowledge, and, thus, this situation triggers an information need. These ‘situational dimensions’ build the ‘situational predictors’ of information seeking and use behaviors, which are being proposed to be included in a UM to support adaptive information access. These situational dimensions will be derived from the user’s problematic situation or the so called, the sense making phenomena, which is explained by the Sense Making theory and methodology (i.e. which is a model, a way of studying information seeking and use). The concept ‘situation’ is consider here as the intersection between the specific situation where the information needs conscious or unconsciously emerged, the questions that the user had in this situation (the gaps) or the knowledge gap that blocked him/her to continue his/her understanding or performance, and the utility that the user hoped to gain of possible answers (i.e. using information) (Fig 2).
Figure 2 – The Sense Making metaphor and how information need is conceptualized in this methodology
This ‘situation’ meaning is supported by the Sense making methodology. Thus, by situational predictors, this research means that they are related to the user’s sense making behavior or process, according to the Sense Making theory and methodology.
Thus, this research applies a constructivist approach to user models for adaptive systems, and this proposal changes the focus of the adaptation from being based on user’s characteristics to be based on the characteristics of information need, seeking and use situation.
At the individual level, people with the same demographic characteristics have more probability of sharing certain situational conditions, but it is less probably that they will “actually process and make sense of information in the same way, because they see and define their situations very differently” (Dervin, 2003, p.56]. Within this context the situational predictors explain why identical users (group) or even the same person exhibit different ways to obtain information in different times to complete a single task or similar tasks and why they change them. In a given situation, a user in her process of seeking information can have different levels of knowledge background, experience with the system, affective reactions, cognitive internal and external resources and of perception of the problem to be solved or of the gap that she may be facing. A researcher can need to find a focus for a topic in a given situation and then need to confirm ideas and inferences in another situation, both related to the same topic. The same researcher facing another topic may need just to clarify her doubts or to choose between two options. Two identical researchers with the same task, goal, background and characteristics, facing a situation of having to find information to confirm a hypothesis, may have different information needs, seeking and use behaviors.
Some of the Sense-Making studies of information seeking demonstrate that the situational characteristics explain far more variance in information seeking and use than demographic descriptors of individuals include the following: Cheuk & Dervin, 1999; Dervin & Shields, 1999; Nilan & Dervin, 1999; Atwood & Dervin, 1982; Dervin, Jacobson & Nilan, 1982; Dervin, Nilan & Jacobson, 1981; and Dervin, Harlock, Atwood, & Garzona, 1980. The user model with the situational predictors proposed by this research is called ‘situational user model’ and it will be compound of the most effective types of information seeking and use predictors (i.e. identified in the data collection and analysis). This ‘situational user model’ will be implanted in a digital library, and will also demand the execution of fragment indexing (using text tiling or text mining techniques) and also semantic indexing. The research outcomes will be a situational adaptive-user model and methodology to increase the match between needs and information accessed and acquired.
4 Research Questions
• How can the user-adaptive interaction respect and attend the human phenomena of constructing meaning or sense?
• How can the adaptivity strategies and decision consider the information needs and behavior differences that exist among users?
• How the information seeking and use behaviors characteristics identified by the Sense Making methodology, can be effectively modeled?
• How the adaptivity predictors in the UM can be enriched in order no increase the closeness and coherence of the adaptivity strategies and results to the user’s sense making or informing process? • What are the most effective situational or sense making predictors that should be used in the user
model? How can the situational or sense making predictors be combined with other structural or traditional predictors, in order to be an integrated and effective set of predictors for adaptive systems?
• How the situational predictors can be inferred by the acquisition of information abut the user made implicitly and/or explicitly?
• How can the situational user model apply and decide the adaptation?
5 Research Objectives
5.1 General objective
Maximize the predictive power of information about the user in adaptive-user models, by applying a constructivist approach and proving the value of such an approach by using the situational or sense making measures as predictors of information needs and behavior, improving and maximizing the probability of information usefulness match to the user’s world.
5.2 Specific objectives
• Identify and systematize the situational or sense making measures that most effectively work as predictors of information needs & behavior.
• Verify the predictive power of situational predictors / combination
• Apply the situational predictors and propose a “Situational Adaptive User model” • Develop a proof of concept/prototype.
• Verify the predictive power of the “Situational Adaptive User model” in helping users gap-bridging in time-space bound situations
• Systematize the “Situational Adaptive User model” and Methodology
6 Research Methodology
This study will adopt the Sense Making assumptions, theoretic perspective, methodological approach and research methods (hereafter Sense Making7 methodology) for studying and modelling information needs, seeking and use, and consequently, to generate the dimensions to be included in the user model. Sense Making methodology enables the study of the sense making processes of individuals and applies a coherent and consistent theory of how to study these. It is a consolidated methodology (30 years, more than 100 researches, Dervin, 2001, p.61) and has been used in many studies about prediction and modelling of information needs and behavior, which is a critical issue for this research. Additionally, Sense Making methodology offers at the same time a qualitative approach for interviewing and data collection; a quantitative approach because it developed procedures and a structure for data analysis and systematization (i.e. Sense-Making metaphor); and it is systematic because the interviewing, listening, understanding and systematization of this understanding of the user is guided by a theory (Dervin, 1989).
Considering this, the research comprises the phases demonstrated in Fig. 5 and explained in the following.
Figure 3 – Overview of the procedures and methodology
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“Sense Making” with initial upper case refers to the methodology, and “sense making” with initials lower case refers to the phenomena.
6.1 Data Collection
6.1.1 Data collection phases and methods
The data collection has three phases (the numbering of the phases corresponds to the numbering in the diagram – Fig 3):
(1) Identification of structural or traditional measures (demography dimensions (e.g. age, education level, gender, year of studies, income, computer ownership and nationality), information literacy levels and domain knowledge. Use of a web-based questionnaire.
(2) Identification of situational or alternative measures (derived from sense making) - These measures are those related to the description of the sense making behaviors and the variables studied in this phase were explained in the item 4, and they are compound of the following situations-gaps-uses categories of variables: actor’s sense making situations, gaps in sense making, actor-defined purpose, information using strategy, information values, information traits. It will be collected using the Sense-Making Micro- Moment Time Line interview.
(9) Identify the effectiveness of the adaptation decisions or the predictive power of the situational user model proposed (Did the adapted information delivered and used helped user overcoming the gap and attended his/her information need?) - In this phase, the prototype of the situational user model is supposed to be installed in an information system (digital library or knowledge – based system).
Data collection pre-interaction with the system - This phase is to identify the context-of-use measures or the context in which the user/respondent is seeking information. The variables will be identified by a questionnaire available before the interaction with the adaptive and he participants will be asked to describe the information problem and the strategy they envisaged for finding the information (browsing or searching, according to the Sense-Making methodology (metaphor).
Data collection during the interaction with the system with the user model proposed by this research (adaptive system) - The objective is to identify what and how the user interacts with this kind of system and to assess the content of the system information for a specific information problem.
Data collection post-interaction with the system - Quantitative and qualitative data will be collected by the use of a web-based semi-structured questionnaire, available after the participant has finished using the adaptive system.
6.2 Data Analyses
The unit of analysis is the sense making instance, which is composed by situations, gaps and uses/helps. 6.2.1 Data analyses methods and procedures
The data analyses are composed of the following activities and techniques (the numbering of the phases corresponds to the numbering in the diagram – Fig 6):
(3) Coding and categorization - Content Analysis for situational predictors and the situational categories of ‘situation-gaps-uses’ are derived deductively.
(4) Verify predictive power of traditional and situational measures. (5) Set the predictors that will be used in the ‘situational user model’ (6) Make the conceptual structure of the ‘situational user model’
(7) Develop a situational user model prototype (computational language)
(8) Apply the situational user model to a system (digital library or a knowledge-based system)
(10) Analysis of the effectiveness of the adaptation decisions or predictive power identified in phase (9).
(11) Design of conclusions and outcomes: situational adaptive user model and Situational adaptivity methodology
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