Recommendation Systems for Online Learning Materials Using Cosine Similarity and Simple Additive Weighting

This study focuses on searching for teaching materials in order to obtain relevant teaching material information appropriately for further use as material for recommendation of course material to students using the Cosine Similarity method and calculating weighting using the Simple Additive Weighting (SAW) method. With the SAW method, 3 criteria and weight values are determined for each attribute, followed by a ranking process. So that in the end the search results that are ranked in the order of similarity and most relevant can be displayed and then selected and used as recommendations in the student's e-learning learning system. From the results of the study, the Cosine Similarity and SAW methods have provided a fairly good/effective recommendation with an average precision of 0.7867 and a recall of 0.766 so that this method is appropriate to be placed in campus e-Learnin

#MPLP: a Comparison of Domain Novice and Expert User-generated Tags in a Minimally Processed Digital Archive

The high costs of creating and maintaining digital archives precluded many archives from providing users with digital content or increasing the amount of digitized materials. Studies have shown users increasingly demand immediate online access to archival materials with detailed descriptions (access points). The adoption of minimal processing to digital archives limits the access points at the folder or series level rather than the item-level description users\u27 desire. User-generated content such as tags, could supplement the minimally processed metadata, though users are reluctant to trust or use unmediated tags. This dissertation project explores the potential for controlling/mediating the supplemental metadata from user-generated tags through inclusion of only expert domain user-generated tags. The study was designed to answer three research questions with two parts each: 1(a) What are the similarities and differences between tags generated by expert and novice users in a minimally processed digital archive?, 1(b) Are there differences between expert and novice users\u27 opinions of the tagging experience and tag creation considerations?, 2(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with metadata in a traditionally processed digital archive?, 2(b) Does user knowledge affect the proportion of tags matching unselected metadata in a minimally processed digital archive?, 3(a) In what ways do tags generated by expert and/or novice users in a minimally processed collection correspond with existing users\u27 search terms in a digital archive?, and 3(b) Does user knowledge affect the proportion of tags matching query terms in a minimally processed digital archive? The dissertation project was a mixed-methods, quasi-experimental design focused on tag generation within a sample minimally processed digital archive. The study used a sample collection of fifteen documents and fifteen photographs. Sixty participants divided into two groups (novices and experts) based on assessed prior knowledge of the sample collection\u27s domain generated tags for fifteen documents and fifteen photographs (a minimum of one tag per object). Participants completed a pre-questionnaire identifying prior knowledge, and use of social tagging and archives. Additionally, participants provided their opinions regarding factors associated with tagging including the tagging experience and considerations while creating tags through structured and open-ended questions in a post-questionnaire. An open-coding analysis of the created tags developed a coding scheme of six major categories and six subcategories. Application of the coding scheme categorized all generated tags. Additional descriptive statistics summarized the number of tags created by each domain group (expert, novice) for all objects and divided by format (photograph, document). T-tests and Chi-square tests explored the associations (and associative strengths) between domain knowledge and the number of tags created or types of tags created for all objects and divided by format. The subsequent analysis compared the tags with the metadata from the existing collection not displayed within the sample collection participants used. Descriptive statistics summarized the proportion of tags matching unselected metadata and Chi-square tests analyzed the findings for associations with domain knowledge. Finally, the author extracted existing users\u27 query terms from one month of server-log data and compared the generated-tags and unselected metadata. Descriptive statistics summarized the proportion of tags and unselected metadata matching query terms, and Chi-square tests analyzed the findings for associations with domain knowledge. Based on the findings, the author discussed the theoretical and practical implications of including social tags within a minimally processed digital archive

LOAN ME THE MONEY: HOW COGNITIVE ABILITIES AND FINANCIAL KNOWLEDGE INFLUENCE CONSUMERS’ INFORMATION BEHAVIORS

For most people, financial well-being depends on the ability to make sound decisions about many aspects of personal finance. This is especially true in the United States (U.S.), when it comes to consumer loan products such as mortgages and student loans. Consumers who lack strong financial knowledge can unwittingly expose themselves to bad information when searching online. Without understanding people’s searching behaviors, information professionals cannot know whether personal finance-related information systems adequately meet the needs of the people using them. Interactive Information Retrieval (IIR) is well-suited to study this, yet there has been little research in this area. One approach that makes sense for studying debt-related information searching is to investigate the role individual differences play in people’s searching. This is because individual differences are testable constructs that can be associated with differences in search performance outcomes. The purpose of this dissertation research is to understand influences that cognitive abilities and financial knowledge have on outcomes related to search, assessment, and mental workload of adults searching online for debt-related personal finance information. A theoretical model is proposed in which financial knowledge acts as a moderating variable on the effect that cognitive abilities have on search and evaluation behaviors as well as mental workload. The results of the study were mixed. The testing of hypotheses on the model were unsuccessful and provide information for informing future model designs and hypothesis development. The qualitative portion of the study provided numerous insights, including that the topic of personal finance, specifically in the realm of financial loans such as mortgages, student loans, and payday loans, is more challenging for people than they realize. Participants reported low prior knowledge of all task topics and used simple search strategies such as avoiding advertisements on search engine results pages (SERPs), relying heavily on the first SERP result, and reformulating queries rather than investigating SERPs at deeper levels. Participants rated most webpages they found as relevant or very relevant but expert assessors rated most of those same pages as only somewhat relevant or not relevant. The findings have numerous implications and point to key areas for further research.Doctor of Philosoph

Popularität und Relevanz in der Suche

In diesem Open-Access-Buch wird mithilfe eines großangelegten Online-Experiments untersucht, wie sich die Anzeige von Zitationen oder Downloads auf die Relevanzbewertung in akademischen Suchsystemenauswirkt. Bei der Suche nach Informationen verwenden Menschen diverse Kriterien, anhand derer sie die Relevanz der Suchergebnisse bewerten. In diesem Buch wird erstmals eine systematische Übersicht über die Einflüsse im Prozess der Relevanzbewertung von Suchergebnissen in akademischen Suchsystemen aufgezeigt. Zudem wird ein anspruchsvolles und komplexes Methodenframework zur experimentellen Untersuchung von Relevanzkriterien vorgestellt. Dieses eignet sich für die weitergehende Erforschung von Relevanzkriterien im informationswissenschaftlichen Bereich