A Large-Scale, Open-Domain, Mixed-Interface Dialogue-Based ITS for STEM

We present Korbit, a large-scale, open-domain, mixed-interface, dialogue-based intelligent tutoring system (ITS). Korbit uses machine learning, natural language processing and reinforcement learning to provide interactive, personalized learning online. Korbit has been designed to easily scale to thousands of subjects, by automating, standardizing and simplifying the content creation process. Unlike other ITS, a teacher can develop new learning modules for Korbit in a matter of hours. To facilitate learning across a widerange of STEM subjects, Korbit uses a mixed-interface, which includes videos, interactive dialogue-based exercises, question-answering, conceptual diagrams, mathematical exercises and gamification elements. Korbit has been built to scale to millions of students, by utilizing a state-of-the-art cloud-based micro-service architecture. Korbit launched its first course in 2019 on machine learning, and since then over 7,000 students have enrolled. Although Korbit was designed to be open-domain and highly scalable, A/B testing experiments with real-world students demonstrate that both student learning outcomes and student motivation are substantially improved compared to typical online courses

Artificial Intelligence And Big Data Technologies To Close The Achievement Gap.

We observe achievement gaps even in rich western countries, such as the UK, which in principle have the resources as well as the social and technical infrastructure to provide a better deal for all learners. The reasons for such gaps are complex and include the social and material poverty of some learners with their resulting other deficits, as well as failure by government to allocate sufficient resources to remedy the situation. On the supply side of the equation, a single teacher or university lecturer, even helped by a classroom assistant or tutorial assistant, cannot give each learner the kind of one-to-one attention that would really help to boost both their motivation and their attainment in ways that might mitigate the achievement gap. In this chapter Benedict du Boulay, Alexandra Poulovassilis, Wayne Holmes, and Manolis Mavrikis argue that we now have the technologies to assist both educators and learners, most commonly in science, technology, engineering and mathematics subjects (STEM), at least some of the time. We present case studies from the fields of Artificial Intelligence in Education (AIED) and Big Data. We look at how they can be used to provide personalised support for students and demonstrate that they are not designed to replace the teacher. In addition, we also describe tools for teachers to increase their awareness and, ultimately, free up time for them to provide nuanced, individualised support even in large cohorts

Survey on Evaluation Methods for Dialogue Systems

In this paper we survey the methods and concepts developed for the evaluation of dialogue systems. Evaluation is a crucial part during the development process. Often, dialogue systems are evaluated by means of human evaluations and questionnaires. However, this tends to be very cost and time intensive. Thus, much work has been put into finding methods, which allow to reduce the involvement of human labour. In this survey, we present the main concepts and methods. For this, we differentiate between the various classes of dialogue systems (task-oriented dialogue systems, conversational dialogue systems, and question-answering dialogue systems). We cover each class by introducing the main technologies developed for the dialogue systems and then by presenting the evaluation methods regarding this class

The many meanings of collective action: lessons on enhancing gender inclusion and equity in watershed management

"Collective action in agriculture and natural resource management is all too often perceived of in terms of the mere number of participants, with little consideration given to who participates, why, and the outcomes of inequitable participation. The literature is replete with cases of how uncritical approaches to participation structure positions of privilege vis-à-vis project benefits and the natural resource base. Yet lessons on how to engage with local communities in ways that promote equitable participation of women, the poor and other stakeholders are only now coming to light. This paper focuses on approaches under development under the rubric of the African Highlands Initiative to bring collective action principles to bear on gender-equitable change processes in natural resource management. The paper utilizes a number of case studies to illustrate the relative strengths and weaknesses of different approaches for enhancing gender inclusion and equity throughout the stages of problem diagnosis, planning and monitoring. The analysis suggests that an arbitrary definition of collective action is insufficient for assessing the relative strengths and weaknesses of different approaches, and that method evaluation should consider the different forms that collective action can take. A typology of different forms of collective action is proposed, and then utilized to assess the relative strengths and weaknesses of different approaches for fostering gender inclusion and equity in watershed management." Authors' AbstractNatural resource management, Gender, Water, Collective action, Community organizations, Community-based organizations, Women, Watershed management,

Rhetorical relationships with students: A higher education case study of perceptions of online assessment in mathematics

Some students perceive that online assessment does not provide for a true reflection of their work effort. This article reports on a collaborative international project between two higher education institutions with the aim of researching issues relating to engineering student perceptions with respect to online assessment of mathematics. It provides a comparison between students of similar educational standing in Finland and Ireland. The students undertook to complete questionnaires and a sample of students was selected to participate in several group discussion interviews. Evidence from the data suggests that many of the students demonstrate low levels of confidence, do not display knowledge of continuous assessment processes and perceive many barriers when confronted with online assessment in their first semester. Alternative perspectives were sought from lecturers by means of individual interviews. The research indicates that perceptions of effort and reward as seen by students are at variance with those held by lecturers. The study offers a brief insight into the thinking of students in the first year of their engineering mathematics course. It may be suggested that alternative approaches to curriculum and pedagogical design are necessary to alleviate student concerns

Proceedings of QG2010: The Third Workshop on Question Generation

These are the peer-reviewed proceedings of "QG2010, The Third Workshop on Question Generation". The workshop included a special track for "QGSTEC2010: The First Question Generation Shared Task and Evaluation Challenge". QG2010 was held as part of The Tenth International Conference on Intelligent Tutoring Systems (ITS2010)

Characterizing Productive Perseverance Using Sensor-Free Detectors of Student Knowledge, Behavior, and Affect

Failure is a necessary step in the process of learning. For this reason, there has been a myriad of research dedicated to the study of student perseverance in the presence of failure, leading to several commonly-cited theories and frameworks to characterize productive and unproductive representations of the construct of persistence. While researchers are in agreement that it is important for students to persist when struggling to learn new material, there can be both positive and negative aspects of persistence. What is it, then, that separates productive from unproductive persistence? The purpose of this work is to address this question through the development, extension, and study of data-driven models of student affect, behavior, and knowledge. The increased adoption of computer-based learning platforms in real classrooms has led to unique opportunities to study student learning at both fine levels of granularity and longitudinally at scale. Prior work has leveraged machine learning methods, existing learning theory, and previous education research to explore various aspects of student learning. These include the development of sensor-free detectors that utilize only the student interaction data collected through such learning platforms. Building off of the considerable amount of prior research, this work employs state-of-the-art machine learning methods in conjunction with the large scale granular data collected by computer-based learning platforms in alignment with three goals. First, this work focuses on the development of student models that study learning through the use of advancements in student modeling and deep learning methodologies. Second, this dissertation explores the development of tools that incorporate such models to support teachers in taking action in real classrooms to promote productive approaches to learning. Finally, this work aims to complete the loop in utilizing these detector models to better understand the underlying constructs that are being measured through their application and their connection to productive perseverance and commonly-observed learning outcomes

Business schools inside the academy: What are the prospects for interdepartmental research collaboration?

Established literature about the role of business schools tends towards more parochial concerns, such as their need for a more pluralist and socially reflexive mode of knowledge production (Starkey and Tiratsoo 2007; Starkey et al 2009) or the failure of management’s professionalism project expressed through the business school movement (Khurana 2007). When casting their gaze otherwise, academic commentators examine business schools’ weakening links with management practice (Bennis and O’Toole 2005). Our theme makes a novel contribution to the business school literature through exploring prospects for research collaborations with other university departments. We draw upon the case of UK business schools, which are typically university-based (unlike some of their European counterparts), and provide illustrations relating to collaboration with medical schools to make our analytical points. We might expect that business schools and medical schools effectively collaborate given their similar vocational underpinnings, but at the same time, there are significant differences, such as differing paradigms of research and the extent to which the practice fields are professionalised. This means collaboration may prove challenging. In short, the case of collaboration between business schools and medical schools is likely to illuminate the challenges for business schools ‘reaching out’ to other university departments