FALSE CERTAINTY AS AN UNWANTED SIDE EFFECT OF KNOWLEDGE ACQUISITION IN COMPUTER-BASED ONLINE SEARCH AND CONTENT LEARNING

Previous research has shown that learners’ subjective certainty in the assumed correctness of their false answers to a knowledge test increased after online learning. It is unclear, however, 1) whether this False Certainty Effect (FaCE) results from online learning per se, or 2) whether a FaCE results from people confusing their own knowledge with information available on the internet while searching the internet, and 3) whether any topic-directed activity can result in a FaCE, even if it is not obviously topic related. We conducted two computer-based experiments to answer these questions. In Experiment 1, participants (N = 135) were randomly assigned to either an online-search learning condition, a computer-based content-learning condition with preselected learning material, or a computer-based topic-exploration condition with no learning- relevant information. Across all conditions, there was an increase in false certainty after the activity. The FaCE was equally strong in the two learning conditions (online search and content learning) and minimal in the non-learning condition. In Experiment 2 (N = 87), we replicated the FaCE for a learning activity with pre-selected materials but did not find a spill-over effect to an unrelated topic. These results indicate that the FaCE is primarily an unwanted side effect of the knowledge acquisition that arises from brief computer-based learning activitie

“That means nothing to me as a normal person who doesn\u27t know about patents”: Usability testing of Google Patents and Patent Public Search with undergraduate engineering students

Patent searching is an important research tool for undergraduate engineering students, yet it requires special topic knowledge to conduct successfully. Patent database websites have the ability to alleviate or add to the complexity of patent searching, depending on their usability. Prompted by the launch of the US Patent and Trademark Office’s Patent Public Search (PPS) website in early 2022, the authors investigated the usability of PPS and Google Patents. The study\u27s objective was to gain insights into the ways in which the websites of commonly-used patent databases support undergraduate students’ patent searching activities. The study examined students’ performance of typical tasks such as constructing search queries, filtering results, evaluating results, and interpreting classification and citation data. Data was collected via moderated in-person usability testing, following a think-aloud protocol. Usability issues were identified in both websites, though participants unanimously preferred Google Patents due to their familiarity with other Google products and the “cleaner” design of the search interface. Based on the study’s results, the authors offer recommendations for patent literacy instruction for undergraduate students

“That means nothing to me as a normal person who doesn\u27t know about patents”: Usability testing of Google Patents and Patent Public Search with undergraduate engineering students

Patent searching is an important research tool for undergraduate engineering students, yet it requires special topic knowledge to conduct successfully. Patent database websites have the ability to alleviate or add to the complexity of patent searching, depending on their usability. Prompted by the launch of the US Patent and Trademark Office’s Patent Public Search (PPS) website in early 2022, the authors investigated the usability of PPS and Google Patents. The study\u27s objective was to gain insights into the ways in which the websites of commonly-used patent databases support undergraduate students’ patent searching activities. The study examined students’ performance of typical tasks such as constructing search queries, filtering results, evaluating results, and interpreting classification and citation data. Data was collected via moderated in-person usability testing, following a think-aloud protocol. Usability issues were identified in both websites, though participants unanimously preferred Google Patents due to their familiarity with other Google products and the “cleaner” design of the search interface. Based on the study’s results, the authors offer recommendations for patent literacy instruction for undergraduate students

Segmenting Lecture Videos by Topic: From Manual to Automated Methods

More and more universities and corporations are starting to provide videotaped lectures online for knowledge sharing and learning. Segmenting lecture videos into short clips by topic can extract the hidden information structure of the videos and facilitate information searching and learning. Manual segmentation has high accuracy rates but is very labor intensive. In order to develop a high performance automated segmentation method for lecture videos, we conducted a case study to learn the segmentation process of humans and the effective segmentation features used in the process. Based on the findings from the case study, we designed an automated segmentation approach with two phases: initial segmentation and segmentation refinement. The approach combines segmentation features from three information sources of video (speech text transcript, audio and video) and makes use of various knowledge sources such as world knowledge and domain knowledge. Our preliminary results show that the proposed two-phase approach is promising

User experiments with the Eurovision cross-language image retrieval system

In this paper we present Eurovision, a text-based system for cross-language (CL) image retrieval. The system is evaluated by multilingual users for two search tasks with the system configured in English and five other languages. To our knowledge this is the first published set of user experiments for CL image retrieval. We show that: (1) it is possible to create a usable multilingual search engine using little knowledge of any language other than English, (2) categorizing images assists the user's search, and (3) there are differences in the way users search between the proposed search tasks. Based on the two search tasks and user feedback, we describe important aspects of any CL image retrieval system

Searching Ontologies Based on Content: Experiments in the Biomedical Domain

As more ontologies become publicly available, finding the "right" ontologies becomes much harder. In this paper, we address the problem of ontology search: finding a collection of ontologies from an ontology repository that are relevant to the user's query. In particular, we look at the case when users search for ontologies relevant to a particular topic (e.g., an ontology about anatomy). Ontologies that are most relevant to such query often do not have the query term in the names of their concepts (e.g., the Foundational Model of Anatomy ontology does not have the term "anatomy" in any of its concepts' names). Thus, we present a new ontology-search technique that helps users in these types of searches. When looking for ontologies on a particular topic (e.g., anatomy), we retrieve from the Web a collection of terms that represent the given domain (e.g., terms such as body, brain, skin, etc. for anatomy). We then use these terms to expand the user query. We evaluate our algorithm on queries for topics in the biomedical domain against a repository of biomedical ontologies. We use the results obtained from experts in the biomedical-ontology domain as the gold standard. Our experiments demonstrate that using our method for query expansion improves retrieval results by a 113%, compared to the tools that search only for the user query terms and consider only class and property names (like Swoogle). We show 43% improvement for the case where not only class and property names but also property values are taken into account

Selective Metal Cation Capture by Soft Anionic Metal-Organic Frameworks via Drastic Single-Crystal-to-Single-Crystal Transformations

In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, and splitting and merging. The power of the proposed framework is demonstrated on the medical literature corpus concerned with the autism spectrum disorder (ASD) - an increasingly important research subject of significant social and healthcare importance. In addition to the collected ASD literature corpus which we will make freely available, our contributions also include two free online tools we built as aids to ASD researchers. These can be used for semantically meaningful navigation and searching, as well as knowledge discovery from this large and rapidly growing corpus of literature.Comment: In Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 201

Escaping the Trap of too Precise Topic Queries

At the very center of digital mathematics libraries lie controlled vocabularies which qualify the {\it topic} of the documents. These topics are used when submitting a document to a digital mathematics library and to perform searches in a library. The latter are refined by the use of these topics as they allow a precise classification of the mathematics area this document addresses. However, there is a major risk that users employ too precise topics to specify their queries: they may be employing a topic that is only "close-by" but missing to match the right resource. We call this the {\it topic trap}. Indeed, since 2009, this issue has appeared frequently on the i2geo.net platform. Other mathematics portals experience the same phenomenon. An approach to solve this issue is to introduce tolerance in the way queries are understood by the user. In particular, the approach of including fuzzy matches but this introduces noise which may prevent the user of understanding the function of the search engine. In this paper, we propose a way to escape the topic trap by employing the navigation between related topics and the count of search results for each topic. This supports the user in that search for close-by topics is a click away from a previous search. This approach was realized with the i2geo search engine and is described in detail where the relation of being {\it related} is computed by employing textual analysis of the definitions of the concepts fetched from the Wikipedia encyclopedia.Comment: 12 pages, Conference on Intelligent Computer Mathematics 2013 Bath, U