Abstract Learning is at the heart of every progress the human species makes. It is most effective when it considers who we are as individuals, what learning approach we prefer and what we already know to begin with. In the digital age, we strive to capture such information in the form of a digital representation—the so-called learner model—, to tailor learning-related systems to this information and build upon it to create more personalised learning experiences. Over recent years, the proliferation of diverse models across various educational applications and disciplines has made it challenging to access targeted research. In this survey, we aim to address this gap, reviewing the latest advances in learner modelling and conducting a comprehensive analysis of the existing approaches, focusing on developments from 2014 to 2023. With the help of a systematic literature review (SLR), we want to provide designers and developers of learner models with a structured overview and simplified entrance into the topic and the field of learner models. We investigate the question: What do learner models look like and how are they filled, kept up to date and used? To this end, we analyse and classify existing approaches. Our findings provide a comprehensive and structured overview of the field of learner modelling, allowing researchers to navigate and understand the diverse approaches more easily and providing developers of learner models or adaptive systems with a practical tool to access relevant information according to their needs.

Digital study assistants (DSA) aim to support the challenging tasks of searching for relevant information and organizing this data for individual study planning. Although such systems are characterized by complex process flows, their user experience (UX) has rarely been examined adequately. This research comprehensively analyzes the UX of such a system at the University of Bamberg, which includes short-term planning for one semester as well as the distinctive feature of long-term planning beyond one semester. Via remote usability testing including an online questionnaire and involving 26 participants, this study explores students’ interactions with the system and evaluates the impact of the system design on the UX, identifying strengths and weaknesses. The study has revealed that participants faced major challenges related to complex processes resulting from the lack of functional and terminological differentiation between short- and long-term study planning for users. In addition, certain features, including extended search options, were hidden and could not be found immediately. Derived from these findings, we present nine design principles to guide the development of effective DSA and similar support systems.

The research data landscape of the arts and humanities is characterized by a high degree of heterogeneity. To improve interoperability, recent initiatives and research infrastructures are encouraging the use of standards and best practices. However, custom data models are often considered necessary to exactly reflect the requirements of a particular collection or research project. To address the needs of scholars in the arts and humanities for a composition of research data irrespective of the degree of structuredness and standardization, we propose a concept on the basis of formal languages, which facilitates declarative data modeling by respective domain experts. By identifying and defining grammatical patterns and deriving transformation functions, the structure of data is generated or extended in accordance with the particular context and needs of the domain.

The efficient execution of multi-criteria queries has gained increasing interest over the last years. In the present paper we propose an R-tree based approach for queries addressing textual as well as geographic filter conditions. Whereas most previous approaches use an index structure optimised for a single criterion adding special treatment for the other criterion at the leaf nodes or end points of this index structure, our approach uses a deeper integration. In short, R-trees are maintained for certain subsets of the whole term set. Furthermore, in each of these R-trees bit sets are used within the nodes to indicate whether entries for the terms associated with the single bits can be found in the corresponding sub-tree. Our index structure aims to be both, time and space efficient. The paper investigates the efficiency and applicability of the proposed index structure via practical experiments based on real-world and synthetic data.

Determining the bounds of geographic regions is an important task for geographic search engines which use concept@location-type of queries. The location a user specifies is often not contained in the underlying gazetteer or geographic database, which might be due to vernacular descriptions of regions or because the location is not a geographic region in the narrow sense, which is the case in queries like campground near theme park. In the present paper we describe different ways for automatically determining a geographic footprint for those locations so that a geographic search engine is able to deal with all kinds of location-descriptions. The same approaches can be used to visualize the geographic correlation of arbitrary terms, like the visualization of the spread of certain colloquialisms.The basic idea is to mine locations found in the top documents resulting from a query consisting of the terms the user has chosen to specify the location. We describe how this can be done using kernel density estimation, clustering and a combination thereof.

In peer-to-peer (P2P) networks, computers with equal rights form a logical (overlay) network in order to provide a common service that lies beyond the capacity of every single participant. Efficient similarity search is generally recognized as a frontier in research about P2P systems. One way to address it is using data source selection based approaches where peers summarize the data they contribute to the network, generating typically one summary per peer. When processing queries, these summaries are used to choose the peers (data sources) that are most likely to contribute to the query result. Only those data sources are contacted. There are two main contributions of this paper. We extend earlier work, adding a data source selection method for high-dimensional vector data, comparing different peer ranking schemes. More importantly, we present a method that uses progressive stepwise data exchange between peers to better each peer's summary and therefore improve the system's performance

Much of the present P2P-IR literature is focused on distributed indexing structures. Within this paper, we present an approach based on the replication of peer data summaries via rumor spreading and multicast in a structured overlay.We will describe Rumorama, a P2P framework for similar-ity queries inspired by GlOSS and CORI and their P2P-adaptation, PlanetP. Rumorama achieves a hierarchization of PlanetP-like summary-based P2P-IR networks. In a Rumorama network, each peer views the network as a small PlanetP network with connections to peers that see other small PlanetP networks. One important aspect is that each peer can choose the size of the PlanetP network it wants to see according to its local processing power and bandwidth. Even in this adaptive environment, Rumorama manages to process a query such that the summary of each peer is considered exactly once in a network without churn. However, the actual number of peers to be contacted for a query is a small fraction of the total number of peers in the network.Within this article, we present the Rumorama base protocol, as well as experiments demonstrating the scalability and viability of the approach under churn.

Query processing on partly inaccessible databases generally does not yield exact, but vague result sets. A good notion of vague sets fulfills two aims: It keeps the degree of vagueness of the query result as small as possible, and it clarifies the degree of and the reasons for the vagueness to the end user. The first goal requires a good internal representation, while the second goal requires a good external representation of a vague set. In this paper, we present a novel calculus for expressive vague sets that meets both requirements. This is the first approach that is well suited for both internal and external representation of vagueness induced by partial inaccessibility. It consists of a data representation that is capable of holding all the necessary information. Complementary, we have accordingly adapted the usual query language operations. These adaptations are independent of a concrete query language, to make them applicable to most existing query languages. The adapted operations minimize the vagueness of the result, propagate the reasons of uncertainty of the individual vague candidates, and compute an expressive description of the missing elements.

An important characteristic of distributed object management systems is that due to network or machine failure, the environment may become partitioned into subenvironments that cannot communicate with each other. In some application scenarios, it is important that the subenvironments remain operable even in this case. In particular, queries should be processed in an appropriate way. To this end, the final and all intermediate results of a query in a distributed object management system must be regarded as potentially vague. We propose a hybrid representation for vague sets and vague multisets designed for this application context. The representation consists of an enumerating part, which contains the elements we could access during query processing, and a descriptive part, which describes the relevant elements we could not access. We introduce propagation rules which can be used to minimize the vagueness of a query result represented in this hybrid way. The main advantage of our approach is that the descriptive part of the representation can be used to improve the enumerating part during query processing.

Efficient access structures for similarity queries on feature vectors are an important research topic for application areas such as multimedia databases, molecular biology or time series analysis. Different access structures for high dimensional feature vectors have been proposed, namely: the SS-tree, the VAMSplit R-tree, the TV-tree, the SR-tree and the X-tree. All these access structures are derived from the R-tree. As a consequence, the fanout of the directory of these access structures decreases drastically for higher dimensions. Therefore we argue that the R-tree is not the best possible starting point for the derivation of an access structure for high-dimensional data. We show that k-d-tree-based access structures are at least as well suited for this application area and we introduce the LSD/sup h/-tree as an example for such a k-d-tree-based access structure for high-dimensional feature vectors. We describe the algorithms for the LSD/sup h/-tree and present experimental results comparing the LSD/sup h/-tree and the X-tree.

Index structures in object-oriented database management systems should support selections not only with respect to physical object attributes, but also with respect to derived attributes. A simple example arises, if we assume the object types Company , Division,andEmployee, with the relationships has division from Company to Division,andemploys from Division to Employee. In this case the index structure should allow to support queries for companies specifying the number of employees of the company. Unfortunately, there is one main problem with index structures addressing derived attributes, namely the question: Which entries in which index structures have to be updated after a certain update in the object base? In the example above, the creation of a new employee must trigger an index update for the corresponding company. In the present paper we propose a practical solution for this problem. This solution is based on so-called index update definitions which comprise (1) an event desc...

Article Free Access Share on Adapting a spatial access structure for document representations in vector space Author: Andreas Henrich Universität Siegen, Praktische Informatik, D-57068 Siegen, Germany Universität Siegen, Praktische Informatik, D-57068 Siegen, GermanyView Profile Authors Info & Claims CIKM '96: Proceedings of the fifth international conference on Information and knowledge managementNovember 1996 Pages 19–26https://doi.org/10.1145/238355.238367Online:12 November 1996Publication History 8citation342DownloadsMetricsTotal Citations8Total Downloads342Last 12 Months5Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

In recent years, various k-d-tree based multidimensional access structures have been proposed. All these structures share an average bucket utilization of at most ln 2/spl ap/69.3%. We present two algorithms which perform local redistributions of objects to improve the storage utilization of these access structures. We show that under fair conditions a good improvement algorithm can save up to 20% of space and up to 15% of query processing time. On the other hand we also show that a local redistribution scheme designed without care, can improve the storage utilization and at the same time worsen the performance of range queries drastically. Furthermore we show the dependencies between split strategies and local redistribution schemes and the general limitations which can be derived from these dependencies.

Modern system development environments usually deploy the object management facilities of a so-called repository to store the documents created and maintained during system

An interesting research area for distributed database systems is inaccessibility.Even if part of the database is inaccessible, a query should be evaluated reasonably.This means, that appropriate rules for propagation and minimization of errors induced by inaccessibility are needed.Furthermore, the result of a query in a distributed database system must be regarded to be potentially vague.In this paper we present a hybrid representation for vague sets, that consists of(1) an enumerating part, which contains the elements we could access during query processing, and (2) a descriptive part, which describes the relevant elements we could not access.Further we introduce propagation rules, which can be used to minimize the vagueness of a query result represented in this hybrid way.Thereby, we propose different levels of accuracy for the descriptive part.As far as we know this is the first approach using a descriptive part to qualify the missing elements of a result.An example for the usefulness of the descriptive part arises, if we have to calculate the intersection of two subqueries.Here, the descriptive part of the first operand can be used to check for each element of the second operand, whether it should be part of the first operand, and vice versa.