Employee engagement

Résumé

Employee engagement is a fundamental concept in the effort to understand and describe, both qualitatively and quantitatively, the nature of the relationship between an organization and its employees. An "engaged employee" is defined as one who is fully absorbed by and enthusiastic about their work and so takes positive action to further the organization's reputation and interests. An engaged employee has a positive attitude towards the organization and its values. In contrast, a disengaged employee may range from someone doing the bare minimum at work (aka 'coasting'), up to an employee who is actively damaging the company's work output and reputation. An organization with "high" employee engagement might therefore be expected to outperform those with "low" employee engagement. Employee engagement first appeared as a concept in management theory in the 1990s, becoming widespread in management practice in the 2000s, but it remains contested. Despite academic critiques, employee enga

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Publications associées (9)

A Speech-based Productive Engagement Metric for Real-time Human-Robot Interaction in Collaborative Educational Contexts

Barbara Bruno, Pierre Dillenbourg, Jauwairia Nasir

One of the major challenges in educational contexts is to correctly interpret the student's learning process in real-time, which is a necessary pre-requisite for timely and appropriate interventions. In educational HRI, multiple solutions have been proposed to endow robots with this key ability, typically relying on observable proxies such as in-task performance, affective behavior, engagement with the robot, etc. In this methodology paper, we propose and validate a metric for the real-time analysis of the behaviour of learners, allowing to assess whether they are engaged in meaningful learning behaviours. Specifically, building on the previously proposed concept of Productive Engagement, that inherently links learning with engagement, we hereby propose methods to quantify and compute it reliably in real-time. The training and testing of the methods is done using the open access PE-HRI-temporal dataset, that provides team level multi-modal temporal behavioral data, built from a study done with 68 students (34 teams) in a collaborative human-human educational activity mediated by a robot.

2022

Chargement

Personalized Productive Engagement Recognition in Robot-Mediated Collaborative Learning

Barbara Bruno, Jauwairia Nasir

In this paper, we propose and compare personalized models for Productive Engagement (PE) recognition. PE is defined as the level of engagement that maximizes learning. Previously, in the context of robot-mediated collaborative learning, a framework of productive engagement was developed by utilizing multimodal data of 32 dyads and learning profiles, namely, Expressive Explorers (EE), Calm Tinkerers (CT), and Silent Wanderers (SW) were identified which categorize learners according to their learning gain. Within the same framework, a PE score was constructed in a non-supervised manner for real-time evaluation. Here, we use these profiles and the PE score within an AutoML deep learning framework to personalize PE models. We investigate two approaches for this purpose: (1) Single-task Deep Neural Architecture Search (ST-NAS), and (2) Multitask NAS (MT-NAS). In the former approach, personalized models for each learner profile are learned from multimodal features and compared to non-personalized models. In the MT-NAS approach, we investigate whether jointly classifying the learners' profiles with the engagement score through multi-task learning would serve as an implicit personalization of PE. Moreover, we compare the predictive power of two types of features: incremental and non-incremental features. Non-incremental features correspond to features computed from the participant's behaviours in fixed time windows. Incremental features are computed by accounting to the behaviour from the beginning of the learning activity till the time window where productive engagement is observed. Our experimental results show that (1) personalized models improve the recognition performance with respect to non-personalized models when training models for the gainer vs. non-gainer groups, (2) multitask NAS (implicit personalization) also outperforms non-personalized models, (3) the speech modality has high contribution towards prediction, and (4) non-incremental features outperform the incremental ones overall.

2022

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In educational HRI, it is generally believed that a robots behavior has a direct effect on the engagement of a user with the robot, the task at hand and also their partner in case of a collaborative activity. Increasing this engagement is then held responsible for increased learning and productivity. The state of the art usually investigates the relationship between the behaviors of the robot and the engagement state of the user while assuming a linear relationship between engagement and the end goal: learning. However, is it correct to assume that to maximise learning, one needs to maximise engagement? Furthermore, conventional supervised models of engagement require human annotators to get labels. This is not only laborious but also introduces further subjectivity in an already subjective construct of engagement. Can we have machine-learning models for engagement detection where annotations do not rely on human annotators? Looking deeper at the behavioral patterns and the learning outcomes and a performance metric in a multi-modal data set collected in an educational human-human-robot setup with

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students, we observe a hidden link that we term as Productive Engagement. We theorize a robot incorporating this knowledge will 1) distinguish teams based on engagement that is conducive of learning; and 2) adopt behaviors that eventually lead the users to increased learning by means of being productively engaged. Furthermore, this seminal link paves way for machine-learning models in educational HRI with automatic labeling based on the data.

2021

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2021