Tag-based information access in image collections: insights from log and eye-gaze analyses

Coding peekaboom: a gaming mechanism for harvesting programming concepts

There are plentiful online programming resources that enable learners to develop an understanding of conceptual knowledge and practical implementation. However, learners, especially novices, often experience difficulties locating the required information to solve the programming problems. Differ from natural language in syntax and convention, answers for programming languages may not be found just by simple text information retrieval. To address this issue, Coding Peekaboom, a game-based tagging was developed to help adequately index the critical concepts of a code segment. An EEG device was applied to measure participants' mental states to identify their engagement during the gameplay. Study results include the effectiveness of appropriate concepts collected by participants whereas 47.15 concepts were collected on average in a game. The brainwave analysis and the questionnaire results reveal that participants were highly engaged in the tagging task via Coding Peekaboom. Correlations were found between the state of flow and the number of concepts selected, score, and time. Finally, the results of the flow theory and personal traits were reported to reflect the user experiences in the game.

Comparative study of graphic-based tag clouds: theory and experimental evaluation for information search

Purpose

Graphic-based tag clouds aim to visually represent tag content and tag structure, and then to better represent tagged information for later search. However, few studies have clarified the features among varied visualization approaches involved in graphic-based tag clouds and compared them for the purpose of information search.

Design/methodology/approach

After reviewing four kinds of graphic-based tag clouds, an experimental demonstration was conducted in our study to verify how user performs in information search for a general seeking task by using them. Precision ratio, recall ratio, clicks on search and time for search were four variables tested in the experiment. Also, two supplementary tests were respectively carried out to manifest how graphic-based tag clouds contributed to the identification of target tags and tag clusters.

Findings

The experimental results showed that compared to tag content visual tag structure was more important to find related tags from tag clouds for information search. In addition, tag clouds that visually represented the semantic relationships within tags could make user more confident about their search result and carry out a shorter learning process during searching, which signified a tag-based information search path when visual elements were applied.

Originality/value

This research is one of the first to illustrate systematically the graphic-based tag clouds and their impacts on information search. The research findings could suggest on how to build up more effective and interactive tag clouds and make proposition for the design of search user interface by using graphic-based tag clouds.