This work compares user collaboration with conversational personal assistants vs. teams of expert chatbots. Two studies were performed to investigate whether each approach affects accomplishment of tasks and collaboration costs. Participants interacted with two equivalent financial advice chatbot systems, one composed of a single conversational adviser and the other based on a team of four experts chatbots. Results indicated that users had different forms of experiences but were equally able to achieve their goals. Contrary to the expected, there were evidences that in the teamwork situation that users were more able to predict agent behavior better and did not have an overhead to maintain common ground, indicating similar collaboration costs. The results point towards the feasibility of either of the two approaches for user collaboration with conversational agents.
Azevedo, Roger (McGill University) | Johnson, Amy (University of Memphis) | Burkett, Candice (University of Memphis) | Chauncey, Amber (University of Memphis) | Lintean, Mihai ( University of Memphis ) | Cai, Zhiqiang (University of Memphis) | Rus, Vasile (University of Memphis)
An experiment was conducted to test the efficacy of a new intelligent hypermedia system, MetaTutor, which is intended to prompt and scaffold the use of self-regulated learning (SRL) processes during learning about a human body system. Sixty-eight (N=68) undergraduate students learned about the human circulatory system under one of three conditions: prompt and feedback (PF), prompt-only (PO), and control (C) condition. The PF condition received timely prompts from animated pedagogical agents to engage in planning processes, monitoring processes, and learning strategies and also received immediate directive feedback from the agents concerning the deployment of the processes. The PO condition received the same timely prompts, but did not receive any feedback following the deployment of the processes. Finally, the control condition learned without any assistance from the agents during the learning session. All participants had two hours to learn using a 41-page hypermedia environment which included texts describing and static diagrams depicting various topics concerning the human circulatory system. Results indicate that the PF condition had significantly higher learning efficiency scores, when compared to the control condition. There were no significant differences between the PF and PO conditions. These results are discussed in the context of development of a fully-adaptive hypermedia learning system intended to scaffold self-regulated learning.
In this paper, we have proposed a brain signal classification method, which uses eigenvalues of the covariance matrix as features to classify images (topomaps) created from the brain signals. The signals are recorded during the answering of 2D and 3D questions. The system is used to classify the correct and incorrect answers for both 2D and 3D questions. Using the classification technique, the impacts of 2D and 3D multimedia educational contents on learning, memory retention and recall will be compared. The subjects learn similar 2D and 3D educational contents. Afterwards, subjects are asked 20 multiple-choice questions (MCQs) associated with the contents after thirty minutes (Short-Term Memory) and two months (Long-Term Memory). Eigenvalues features extracted from topomaps images are given to K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classifiers, in order to identify the states of the brain related to incorrect and correct answers. Excellent accuracies obtained by both classifiers and by applying statistical analysis on the results, no significant difference is indicated between 2D and 3D multimedia educational contents on learning, memory retention and recall in both STM and LTM.
Azaria, Amos (Bar Ilan University) | Hassidim, Avinatan (Bar Ilan University) | Kraus, Sarit (Bar Ilan University) | Eshkol, Adi (Viaccess-Orca) | Weintraub, Ofer (Viaccess-Orca) | Netanely, Irit (Viaccess-Orca)
Traditional recommender systems try to provide users with recommendations which maximize the probability that the user will accept them. Recent studies have shown that recommender systems have a positive effect on the provider’s revenue. In this paper we show that by giving a different set of recommendations, the recommendation system can further increase the business’ utility (e.g. revenue), without any significant drop in user satisfaction. Indeed, the recommendation system designer should have in mind both the user, whose taste we need to reveal, and the business, which wants to promote specific content. In order to study these questions, we performed a large body of experiments on Amazon Mechanical Turk. In each of the experiments, we compare a commercial state-of-the-art recommendation engine with a modified recommendation list, which takes into account the utility (or revenue) which the business obtains from each suggestion that is accepted by the user. We show that the modified recommendation list is more desirable for the business, as the end result gives the business a higher utility (or revenue). To study possible longterm effects of giving the user worse suggestions, we asked the users how they perceive the list of recommendation that they received. Our findings are that any difference in user satisfaction between the list is negligible, and not statistically significant. We also uncover a phenomenon where movie consumers prefer watching and even paying for movies that they have already seen in the past than movies that are new to them.
Future Tense is a partnership of Slate, New America, and Arizona State University that examines emerging technologies, public policy, and society. Over two days of testimony before Congress earlier this month, Facebook founder and CEO Mark Zuckerberg dodged a litany of questions from lawmakers about how the data of 87 million Americans ended up in the hands of voter profiling firm Cambridge Analytica. The spectacle put a spotlight on the company's murky data-collection and sharing practices, and sparked a much-needed discussion about if and how to hold companies accountable for their handling of user data. However much deserved, Facebook has, so far, born the brunt of public scrutiny for what has unfortunately become standard practice for web platforms and services. As the Ranking Digital Rights 2018 Corporate Accountability Index--an annual ranking of the some of the world's most powerful internet, mobile, and telecommunications companies that was released this week--shows, companies across the board lack transparency about what user data they collect and share, and tell us alarmingly little about their data-sharing agreements with advertisers or other third parties.