Natural language dialogue for personalized interaction

Zadrozny, Wlodek; Budzikowska, Margo; Chai, Joyce; Kambhatla, Nanda; Lévesque, Sylvie; Nicolov, Nicolas

doi:10.1145/345124.345164

Cited by 39 publications

(22 citation statements)

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“…The most effective way of supporting Human Computer Interaction (HCI) is by allowing users freely express their questions and interest by texting or speaking to the agent [28]. These agents encourage interactions and give the user opportunity to take control of their learning by prompting questions or requesting for hints/ideas to performing a practical task.…”

Section: How Conversation Pedagogical Agent Can Improve Usability Of mentioning

confidence: 99%

Framework for Improving Usability of Learning Management Systems by Integrating Pedagogical Agent

Odhiambo¹,

Okeyo²,

Cheruiyot³

2017

IJCA

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The research intended to develop a framework for improving the usability of Learning Management Systems by integrating pedagogical agent. The study adopted usability heuristics as the key factors for enhancing the usability of an LMS through a conversational pedagogical agent. The first objective explored various techniques available for enhancing the usability of the LMS. The techniques includes Learnability, efficiency, and satisfaction are but a few techniques used to measure usability of a web-based system. The second objective examined a variety of ways the pedagogical agent can improve the usability of the LMS. The research recognized that a pedagogical agent could improve the usability of an LMS by making studying more interesting and increasing student to instructor interaction to support active learning. The third objective was for the designing of a suitable framework for improving the usability of an LMS. The fourth objective intended to evaluate the impact of the computer-based pedagogical agent in the LMS. The research established that the conversational pedagogical agent increased human to computer interaction, makes learning more effective and enjoyable and supports self-paced active learning. An experimental research design was adopted in carrying out the research. A conversational pedagogical agent (Melsyanne) was deployed as a prototype to improve the effective use of an LMS. The target population comprised of 3 HODs, 12 Instructors and 82 Students from three randomly selected higher learning institutions within Nakuru Municipality. Data was collected using questionnaires and analyzed through frequencies and percentages using SPSS. The results were presented using tables, column graphs and pie-charts generated by MS Excel application software. The findings constitute the knowledge pool, from which future research can borrow and add in their research study.

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Section: How Conversation Pedagogical Agent Can Improve Usability Of mentioning

confidence: 99%

Framework for Improving Usability of Learning Management Systems by Integrating Pedagogical Agent

Odhiambo¹,

Okeyo²,

Cheruiyot³

2017

IJCA

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“…Whittaker et al [15] explored the area between the (menu-based, touch-tone, in-turn) and (menu-based, text, in-turn) points. Zadrozny et al [19] explored the portion between the (natural language, voice-response, in-turn) and (natural language, text, in-turn) points. Arons [2] studied interacting with an ATS decision tree through speech input (front, bottom-left corner of Fig.…”

Section: Related Researchmentioning

confidence: 99%

A study of out-of-turn interaction in menu-based, IVR, voicemail systems

Perugini

Anderson

Moroney

2007

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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We present the first user study of out-of-turn interaction in menu-based, interactive voice-response systems. Out-ofturn interaction is a technique which empowers the user (unable to respond to the current prompt) to take the conversational initiative by supplying information that is currently unsolicited, but expected later in the dialog. The technique permits the user to circumvent any flows of navigation hardwired into the design and navigate the menus in a manner which reflects their model of the task. We conducted a laboratory experiment to measure the effect of the use of outof-turn interaction on user performance and preference in a menu-based, voice interface to voicemail. Specifically, we compared two interfaces with the exact same hierarchical menu design: one with the capability of accepting out-ofturn utterances and one without this feature. The results indicate that out-of-turn interaction significantly reduces task completion time, improves usability, and is preferred to the baseline. This research studies an unexplored dimension of the design space for automated telephone services, namely the nature of user-addressable input (utterance) supplied (inturn vs. out-of-turn), in contrast to more traditional dimensions such as input modality (touch-tone vs. text vs. voice) and style of interaction (menu-based vs. natural language).

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“…The search engines usually do not accept natural language like query even though users often prefers to interact with a site by using natural language like query processing system. Zadrozny et al [14] agreed that the best way to facilitate Human Computer Interaction (HCI) is by allowing users "to express their interest, wishes, or queries directly and naturally, by speaking, typing, and pointing". A myriad e-commerce based sites are available from the mobile devices to search and list products online .…”

Section: Introductionmentioning

confidence: 99%

Location Aware Question Answering Based Product Searching in Mobile Handheld Devices

Hossain

Rahman

Tran

et al. 2010

2010 IEEE/ACM 14th International Symposium on Distributed Simulation and Real Time Applications

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In this research we present a question answering based searching technique for location based shopping. A user may ask a question to the system as they naturally ask to a human while the system retrieves the search results by analyzing the given question and the current GPS location. Based on the retrieved results, the system carry out conversation with the user to explicitly understand his/her needs and accordingly filters search results for display. The conversation between the system and the user is based on word co-occurrence keyword extraction and Artificial Intelligence Markup Language (AIML) technique. As per initial experimentation, we found out that the proposed approach of conversation based shopping is appealing and useful to the user.

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Natural language dialogue for personalized interaction

Cited by 39 publications

References 0 publications

Framework for Improving Usability of Learning Management Systems by Integrating Pedagogical Agent

Framework for Improving Usability of Learning Management Systems by Integrating Pedagogical Agent

A study of out-of-turn interaction in menu-based, IVR, voicemail systems

Location Aware Question Answering Based Product Searching in Mobile Handheld Devices

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