Ontology-based recommender system in higher education

International audienceAcademic advising is limited in its ability to assist students in identifying academic pathways. Selecting a major and a university is a challenging process rife with anxiety. Students at high school are not sure how to match their interests with their working future or major. Therefore, high school students need guidance and support. Moreover, students need to filter, prioritize and efficiently get appropriate information from the web in order to solve the problem of information overload. This paper represents an approach for developing ontology-based recommender system improved with machine learning techniques to orient students in higher education. The proposed recommender system is an assessment tool for students' vocational strengths and weaknesses, interests and capabilities. The main objective of our ontology-based recommender system is to identify the student requirements, interests, preferences and capabilities to recommend the appropriate major and university for each one

Early Alert of At-Risk Students: An Ontology-Driven Framework

As higher education continues to adapt to the constantly shifting conditions that society places on institutions, the enigma of student attrition continues to trouble universities. Early alerts for students who are at-risk academically have been introduced as a method for solving student attrition at these institutions. Early alert systems are designed to provide students who are academically at-risk a prompt indication so that they may correct their performance and make progress towards successful semester completion. Many early alert systems have been introduced and implemented at various institutions with varying levels of success. Currently, early alert systems employ different techniques for identifying students that may be at-risk. These techniques range from using machine learning algorithms for predicting students that may become at-risk to more manual methods where the professors are responsible for assigning at-risk tags to students in order to notify the student. This thesis will introduce an ontology-driven framework for early alert reporting of students at-risk. To be more precise we will determine early alerts for students who are at-risk with an ontology-driven framework employing situational awareness. Ontology-driven frameworks allow us to formalize situations in a way that is similar to the human interpretation of situational awareness. The ontology presented will be constructed using OWL the Web Ontology Language. The use of this language will facilitate the description and reasoning of the situation as it is a commonly supported programming language with computable semantics. In this piece we will consider factors such as advisor notes, learning management system interaction, as well as other factors related to student attrition to assign at-risk tags to students who may be at academic risk

How Librarians Can Help Improve Law Journal Publishing

Librarians are well positioned to improve law journal publishing and help it evolve in the ever-changing digital environment. They can provide student editors with advice on a variety of issues such as copyright, data preservation, and version control. Librarians can also help journals adopt technical standards and improve the discoverability and usability of journal content. While few libraries will be able to adopt all these suggestions, a checklist of ideas is provided to help librarians select those that are most suitable to their libraries and journals

Strategic Assessment of Organizational Commitment

The concept of organizational commitment has been widely studied over recent decades, yet it remains one of the most challenging concepts in organizational research. While commitment is understood to be highly valuable in today’s dynamic business environment, its multifaceted nature is not necessarily understood adequately. The purpose of this study was to examine the concept of organizational commitment and its measurement issues within organizations, and to develop a practical evaluation tool for management, which is based on previous scientific research. First, a theoretical framework discussing organizational commitment and engagement was established. Based on the literature research, three ontologies were developed addressing organizational commitment and engagement, as well as academic engagement. The ontologies were constructed as a synthesis of existing theories. With the help of the ontologies and the created evaluation system, it is possible to better understand these concepts, gain a collective view of the organization’s current state and vision for the future, and to open a dialogue between members of the organization regarding their development. The results of the empirical case studies are presented at the end of this thesis, as well as in the attached research papers. The empirical results indicate that, by using these applications, it is possible to gain insights about the respondents’ feelings and aspirations, which can be used to support effective decision-making and as the basis for creating development actions within the organization.Organisaatiositoutumisen käsitettä on tutkittu laajasti kuluneiden vuosikymmenten aikana, kuitenkin se on edelleen yksi organisaatiotutkimuksen haastavimmista käsitteistä. Sitoutuminen on laajalti ymmärretty erittäin tärkeäksi tämän päivän liiketoimintaympäristössä mutta sen moniulotteista luonnetta ei yrityksissä ole välttämättä ymmärretty riittävästi. Tämän tutkimuksen tavoitteena oli tarkastella organisatorisen sitoutumisen käsitettä ja sen mittaamisen ongelmallisuutta sekä kehittää aikaisempaan tieteelliseen tutkimukseen perustuva käytännön sovellus sitoutumisen tason määrittämiseksi. Tutkimuksen ensimmäisessä osassa laadittiin organisaatioon sitoutumista käsittelevä teoreettinen viitekehys, jonka perusteella kehitettiin kolme ontologiaa. Ontologiat käsittelevät organisaation sitoutumista eri näkökulmista sekä opiskelijoiden akateemista sitoutumista. Ontologioiden sekä laaditun arviointijärjestelmän avulla on mahdollista ymmärtää sitoutumiseen liittyviä käsitteitä, saada yhteinen näkemys organisaation nykytilasta ja tulevaisuuden näkemyksestä sekä löytää mahdollisia kehityskohteita. Empiiristen case-tutkimusten tuloksia on esitetty tämän työn loppuosassa sekä liitteenä olevissa tutkimusartikkeleissa. Tulokset osoittavat, että laadittujen sovellusten avulla on mahdollista saada tietoa vastaajien tuntemuksista ja pyrkimyksistä. Tätä tietoa voidaan hyödyntää päätöksenteon tukena sekä perustana kehitystoimien luomiselle.fi=vertaisarvioitu|en=peerReviewed

A Comprehensive Classification of Business Activities in the Market of Intellectual Property Rights-related Services

Technology and intellectual property markets have witnessed great developments in the last few decades. Due to intellectual property rights gaining more importance and technology companies opening up their innovation processes, a wide range of intellectual property rights related services have emerged in the last two decades. The goal of this research is to develop a comprehensive classification system of intellectual property rights related services (IPSC). The classification is created by applying an ontology engineering process. The IPSC consists of 72 various IPR services divided into six main categories (100 Legal Service; 200 IP Consulting; 300 Matchmaking and Trading; 400 IP Portfolio Processing; 500 IPR-related Financial Service; 600 IPR-related Communication Service). The implications of the thesis are directed to policy makers, technology transfer managers, C-level executives and innovation researchers. The IPSC enables practitioners and researchers to organize industry data that can be thereafter analyzed for better strategy and policy making. In addition, this contributes towards organizing a more transparent and single intellectual property market.:Acknowledgements I Abstract II Contents IV List of Figures VI List of Tables VII 1. Introduction 1 1.1. Introduction to Technology Markets 1 1.2. Explanation of Key Concepts 5 1.3. Research Questions and Goals 9 1.4. Readers Guide 13 2. Literature Review 15 2.1. Intellectual Property Markets State of the Art Review 15 2.2. Ontology Engineering State of the Art Review 22 3. Methodology 26 3.1. Methontology 26 3.2. Planning the IPSC 29 3.3. Specification 30 3.4. Conceptualization 31 3.5. Formalization 32 3.6. Integration 32 3.7. Evaluation 33 3.8. Documentation 33 3.9. Realization and Maintenance 33 4. Data description and collection framework 34 5. Applying Methontology 46 5.1. Knowledge Acquisition and Planning the IPSC 46 5.2. Specification 46 5.3. Conceptualization 47 5.4. Formalization 54 100 Legal Service 56 200 IP Consulting 60 300 Matchmaking and Trading 65 400 IP Portfolio Processing 72 500 IPR-related Financial Service 76 600 IPR-related Communication Service 81 5.5. Integration 86 5.6. Evaluation 95 5.7. Documentation 104 5.8. Realization and Maintenance of the IPSC 106 6. Interview Results and Further Discussions 108 6.1. Implications for Industry 108 6.2. Contributions of the IPSC 110 6.3. Limitations of the IPSC and Future Work 112 7. Conclusions 116 References 120 List of experts interviewed and the date of interview 129 Appendices 13

Identified research directions for using manufacturing knowledge earlier in the product lifecycle

Design for manufacturing (DFM), especially the use of manufacturing knowledge to support design decisions, has received attention in the academic domain. However, industry practice has not been studied enough to provide solutions that are mature for industry. The current state of the art for DFM is often rule-based functionality within computer-aided design (CAD) systems that enforce specific design requirements. That rule-based functionality may or may not dynamically affect geometry definition. And, if rule-based functionality exists in the CAD system, it is typically a customisation on a case-by-case basis. Manufacturing knowledge is a phrase with vast meanings, which may include knowledge on the effects of material properties decisions, machine and process capabilities or understanding the unintended consequences of design decisions on manufacturing. One of the DFM questions to answer is: How can manufacturing knowledge, depending on its definition, be used earlier in the product life cycle to enable a more collaborative development environment? This paper will discuss the results of a workshop on manufacturing knowledge that highlights several research questions needing more study. This paper proposes recommendations for investigating the relationship of manufacturing knowledge with shape, behaviour and context characteristics of a product to produce a better understanding of what knowledge is most important. In addition, the proposal includes recommendations for investigating the system-level barriers to reusing manufacturing knowledge and how model-based manufacturing may ease the burden of knowledge sharing. Lastly, the proposal addresses the direction of future research for holistic solutions of using manufacturing knowledge earlier in the product life cycle

Personalizing education with algorithmic course selection

The work presented in this thesis utilizes context-aware recommendation to facilitate personalized education and assist students in selecting courses (or in non-traditional curricula, topics or modules) that meet curricular requirements, leverage their skills and background, and are relevant to their interests. The original research contribution of this thesis is an algorithm that can generate a schedule of courses with consideration of a student\u27s profile, minimization of cost, and complete adherence to institution requirements. The research problem at hand - a constrained optimization problem with potentially conflicting objectives - is solved by first identifying a minimal sets of courses a student can take to graduate and then intelligently placing the selected courses into available semesters. The distinction between the proposed approach and related studies is in its simultaneous achievement of the following: guaranteed fulfillment of curricular requirements; applicability to both traditional and non-traditional curricula; and flexibility in nomenclature - semantics are extracted from syntax to allow the identification of relevant content, despite differences in course or topic titles from one institution to the next. The course selection algorithm presented is developed for the Pervasive Cyberinfrastructure for Personalized eLearning and Instructional Support (PERCEPOLIS), which can assist or supplement the degree planning actions of an academic advisor, with the assurance that recommended selections are always valid. With this algorithm, PERCEPOLIS can recommend the entire trajectory that a student could take to graduation, as opposed to just the next semester, and it does so with consideration of course or topic availability --Abstract, page iii

Framework for Academic Advice through Mobile Applications

The increasing rate of high (secondary) school leavers choosing academic majors to study at the university without proper guidance has most times left students with unfavorable consequences including low grades, extra year(s), the need to switch programs and ultimately having to withdraw from the university. In a bid to proffer a solution to the issue, this research aims to build an expert system that recommends university or academic majors to high school students in developing countries where there is a dearth of human career counselors. This is to reduce the adverse effects caused as a result of wrong choices made by students. A mobile rule-based expert system supported with ontology was developed for easy accessibility by the students

Deep Learning and IoT to Assist Multimorbidity Home Based Healthcare

The authors present a proposal to develop intelligent assisted living environments for home based healthcare in the presence of multimorbidity chronic patients. These environments unite the chronicle patient clinical history sematic representation ICP (Individual Care Process) with the ability of monitoring the living conditions using IoT technologies and events recurring to a fully managed Semantic Web of Things (SWoT) and Machine Learning Algorithms in order to activate the LDC (Less Differentiated Caregiver) for a specific care need. With these capabilities at hand, home based healthcare providing becomes a viable possibility reducing the institutionalization needs. The resulting integrated healthcare framework will provide significant savings while improving the generality of health and satisfaction indicators