Understanding the “Pathway” Towards a Searcher’s Learning Objective

Search systems are often used to support learning-oriented goals. This trend has given rise to the “search-as-learning” movement, which proposes that search systems should be designed to support learning. To this end, an important research question is: How does a searcher’s type of learning objective (LO) influence their trajectory (or pathway ) toward that objective? We report on a lab study (N = 36) in which participants gathered information to meet a specific type of LO. To characterize LOs and pathways , we leveraged Anderson and Krathwohl’s (A&K’s) taxonomy [ 3 ]. A&K’s taxonomy situates LOs at the intersection of two orthogonal dimensions: (1) cognitive process (CP) (remember, understand, apply, analyze, evaluate, and create) and (2) knowledge type (factual, conceptual, procedural, and metacognitive knowledge). Participants completed learning-oriented search tasks that varied along three CPs (apply, evaluate, and create) and three knowledge types (factual, conceptual, and procedural knowledge). A pathway is defined as a sequence of learning instances (e.g., subgoals) that were also each classified into cells from A&K’s taxonomy. Our study used a think-aloud protocol, and pathways were generated through a qualitative analysis of participants’ think-aloud comments and recorded screen activities. We investigate three research questions. First, in RQ1, we study the impact of the LO on pathway characteristics (e.g., pathway length). Second, in RQ2, we study the impact of the LO on the types of A&K cells traversed along the pathway. Third, in RQ3, we study common and uncommon transitions between A&K cells along pathways conditioned on the knowledge type of the objective. We discuss implications of our results for designing search systems to support learning.

Opportunities and Challenges in Code Search Tools

Code search is a core software engineering task. Effective code search tools can help developers substantially improve their software development efficiency and effectiveness. In recent years, many code search studies have leveraged different techniques, such as deep learning and information retrieval approaches, to retrieve expected code from a large-scale codebase. However, there is a lack of a comprehensive comparative summary of existing code search approaches. To understand the research trends in existing code search studies, we systematically reviewed 81 relevant studies. We investigated the publication trends of code search studies, analyzed key components, such as codebase, query, and modeling technique used to build code search tools, and classified existing tools into focusing on supporting seven different search tasks. Based on our findings, we identified a set of outstanding challenges in existing studies and a research roadmap for future code search research.

On the Study of Transformers for Query Suggestion

When conducting a search task, users may find it difficult to articulate their need, even more so when the task is complex. To help them complete their search, search engine usually provide query suggestions. A good query suggestion system requires to model user behavior during the search session. In this article, we study multiple Transformer architectures applied to the query suggestion task and compare them with recurrent neural network (RNN)-based models. We experiment Transformer models with different tokenizers, with different Encoders (large pretrained models or fully trained ones), and with two kinds of architectures (flat or hierarchic). We study the performance and the behaviors of these various models, and observe that Transformer-based models outperform RNN-based ones. We show that while the hierarchical architectures exhibit very good performances for query suggestion, the flat models are more suitable for complex and long search tasks. Finally, we investigate the flat models behavior and demonstrate that they indeed learn to recover the hierarchy of a search session.

Clustering-Based Visual Interfaces for Presentation of Web Search Results: An Empirical Investigation Ozgur

The result of a typical web search is often overwhelming. It is very difficult to explore the textual listing of the resulting documents, which may be in the thousands. In order to improve the utility of the search experience, we explore presenting search results through clustering and a zoomable two-dimensional map (zoomable treemap). Furthermore, we apply the fisheye view technique to this map of web search clusters to provide details in context. In this study, we report on our evaluation of these presentation features. The particular interfaces evaluated were: (1) a textual list, (2) a zoomable two-dimensional map of the clustered results, and (3) a fisheye version of the zoomable two dimensional map where the results were clustered. We found that subjects completed search tasks faster with the visual interfaces than with the textual interface, and faster with the fisheye interface than just the zoomable interface. Based on the findings, we conclude that there is promise in the use of clustering and visualization with a fisheye zooming capability in the exploration of web search results.

Clustering-Based Visual Interfaces for Presentation of Web Search Results: An Empirical Investigation Ozgur

The result of a typical web search is often overwhelming. It is very difficult to explore the textual listing of the resulting documents, which may be in the thousands. In order to improve the utility of the search experience, we explore presenting search results through clustering and a zoomable two-dimensional map (zoomable treemap). Furthermore, we apply the fisheye view technique to this map of web search clusters to provide details in context. In this study, we report on our evaluation of these presentation features. The particular interfaces evaluated were: (1) a textual list, (2) a zoomable two-dimensional map of the clustered results, and (3) a fisheye version of the zoomable two dimensional map where the results were clustered. We found that subjects completed search tasks faster with the visual interfaces than with the textual interface, and faster with the fisheye interface than just the zoomable interface. Based on the findings, we conclude that there is promise in the use of clustering and visualization with a fisheye zooming capability in the exploration of web search results.

Characterising users’ task completion process in learning-related tasks:A search pace model

This study investigated users’ searching, reading and writing interactions and their activity transitions during task completion process when users were collecting information for learning-related search tasks. Task completion process was defined as the process users started to search till the time when they have collected enough information to accomplish the search tasks. The data analysis was conducted from a new process perspective through synthesising macro- and micro-process levels. Four evenly distributed stages were divided according to the total task completion time in each search session. Our results demonstrated that users generally experienced three sub-processes during task completion process: exploration, accumulation and composition/reporting. Exploration sub-process is basically the first quarter of the total task completion time, during which users often issue more queries and visit more search engine result pages (SERPs) to collect information, and the dominant activity transition is switching between searching and reading; accumulation sub-process is mainly the second and third quarters of the total task completion time, during which they visit more unique content pages, have more revisits per content page, and they switch between reading and writing activities frequently; the last stage is composition/reporting sub-process, which is dominated by writing, and users often switch between writing and reading, and between writing and searching. Based on these findings, we propose a search pace model to describe how users proceed from the beginning to the end of task completion process in these three sub-processes. The methodology applied has been proved to be effective to examine users’ interaction behaviours from the process perspective on both the micro- and macro-levels. The findings of this article help us understand how users proceed their dynamic searching, reading and writing behaviours for learning-related tasks, and also have implications for the design of search systems that support learning-related tasks.

Use of mind mapping in search process to clarify information needs and improve search satisfaction

A mind map is an approach to the organisation of the human mind that prepares the ground for thinking. Inspired by the function of the mind in handling a situation, this article reports on an empirical study that evaluated the efficiency of mind map techniques and tools in formulating and refining information needs. The study examined graduate students’ Internet information searching. Two simulated search tasks were completed by participants in two search sessions. The results revealed no statistically significant difference between searching with a mind map and without a mind map, and therefore, no advantage could be found for using a mind map in the search process. Participants were happier with their search session when not using mind maps; mind map might help information need clarification, but it is a barrier to interaction and serendipity retrieval. However, this could be due to the search setting where the mind map had to be used as a separate tool and not an integrated component of the search system. The article also discusses some potential benefits of mind mapping for searching.

Voice Assistant vs. Chatbot – Examining the Fit Between Conversational Agents’ Interaction Modalities and Information Search Tasks

Owing to technological advancements in artificial intelligence, voice assistants (VAs) offer speech as a new interaction modality. Compared to text-based interaction, speech is natural and intuitive, which is why companies use VAs in customer service. However, we do not yet know for which kinds of tasks speech is beneficial. Drawing on task-technology fit theory, we present a research model to examine the applicability of VAs to different tasks. To test this model, we conducted a laboratory experiment with 116 participants who had to complete an information search task with a VA or a chatbot. The results show that speech exhibits higher perceived efficiency, lower cognitive effort, higher enjoyment, and higher service satisfaction than text-based interaction. We also find that these effects depend on the task’s goal-directedness. These findings extend task-technology fit theory to customers’ choice of interaction modalities and inform practitioners about the use of VAs for information search tasks.

Characterizing the Influence of Confirmation Bias on Web Search Behavior

In this study, we analyzed the relationship between confirmation bias, which causes people to preferentially view information that supports their opinions and beliefs, and web search behavior. In an online user study, we controlled confirmation bias by presenting prior information to participants that manipulated their impressions of health search topics and analyzed their behavioral logs during web search tasks. We found that web search users with poor health literacy and negative prior beliefs about the health search topic did not spend time examining the list of web search results, and these users demonstrated bias in webpage selection. In contrast, web search users with high health literacy and negative prior beliefs about the search topic spent more time examining the list of web search results. In addition, these users attempted to browse webpages that present different opinions. No significant difference in web search behavior was observed between users with positive prior beliefs about the search topic and those with neutral belief.