Structured Assessment on Learning Progress

This paper presented a novel technique and practice of the assessment of learning progress of university students in an engineering discipline. Instead of measuring the effectiveness of accumulation of specific knowledge, the newly developed assessment technique evaluates the development of the intelligence of the students. The key components of the proposed technique are a performance-based method for the estimation of the intelligence level and a cognitive mental faculty-oriented decomposition method to determine the intelligence contribution factors for learning subjects and exam questions. The proposed technique was applied to assess the learning progress of a group of university students in the field of automation, and the results from test agreed with the expectation well

Multi-Armed Bandits for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by students, taking into account the limited time and motivational resources. At a given point in time, the system proposes to the students the activity which makes them progress faster. We introduce two algorithms that rely on the empirical estimation of the learning progress, RiARiT that uses information about the difficulty of each exercise and ZPDES that uses much less knowledge about the problem. The system is based on the combination of three approaches. First, it leverages recent models of intrinsically motivated learning by transposing them to active teaching, relying on empirical estimation of learning progress provided by specific activities to particular students. Second, it uses state-of-the-art Multi-Arm Bandit (MAB) techniques to efficiently manage the exploration/exploitation challenge of this optimization process. Third, it leverages expert knowledge to constrain and bootstrap initial exploration of the MAB, while requiring only coarse guidance information of the expert and allowing the system to deal with didactic gaps in its knowledge. The system is evaluated in a scenario where 7-8 year old schoolchildren learn how to decompose numbers while manipulating money. Systematic experiments are presented with simulated students, followed by results of a user study across a population of 400 school children

Intelligent Adaptive Curiosity: a source of Self-Development

This paper presents the mechanism of Intelligent Adaptive Curiosity. This is a drive which pushes the robot towards situations in which it maximizes its learning progress. It makes the robot focus on situations which are neither too predictable nor too unpredictable. This mechanism is a source of self-development for the robot: the complexity of its activity autonomously increases. Indeed, we show that it first spends time in situations which are easy to learn, then shifts progressively its attention to situations of increasing difficulty, avoiding situations in which nothing can be learnt

Bounded Learning Efficiency and Sources of Firm Level Productivity Growth in Colombian Food Manufacturing Industry

The measurement of productivity fluctuations has been the focus of decades-long interest. In addition to broad structural forces driving productivity changes, there is more recent interest in measuring and identifying the heterogeneous forces driving these changes. A major force is learning-by-doing which is used by economists to describe the phenomenon of productivity growth arising from the accumulation of production experience by a firm. This paper proposes a bounded learning concept with the learning progress function characterized by the degree of efficiency and the specification of the learning progress as a logistic function capturing both the slow start-up and the limit in learning progress. The inter-firm learning inefficiency is defined as the inability of a firm to reach the optimal plateau relative to the ‘best practice’ firm from the set of comparable firms. We further differentiate learning efficiency from the technical efficiency. The key contribution of this research is to provide a measure the firm’s movement along the learning progress curve and explain the existence of firm-level heterogeneity in learning. The time varying technical efficiency is estimated based on stochastic production frontier methods and firm-specific learning efficiency is disentangled using the residual of the production frontier (productivity).The model is then used to decompose the factor productivity growth into components associated with learning, scale, technical efficiency, technological change and change in allocative efficiency. This productivity growth decomposition provides useful information and policy level insight in firm-level productivity analysis. The major econometric issue in production function estimation is the possibility that there are some forces influencing production that are only observed by the firm and not by the econometrician. With firm input use being endogenous, inputs might be correlated with unobserved productivity shocks. The measure of technical efficiency by estimating the production frontier directly in presence of endogeneity of input choice can be biased in the sense that the measure of efficiency favors the firms employing higher levels of inputs. The Levinsohn and Petrin (2003) approach is extended to overcome this simultaneity problem in stochastic production frontier estimation to generate consistent estimates of production parameters and technical efficiency. The model is applied to plant-level panel data on Colombian food manufacturing sector. The dataset is unique longitudinal data on firms in the sense that it has information on both plant-specific physical quantities and prices for both outputs and inputs. In contrast to most of the existing literature which measure productivity by deflating sales by an industry-level price index, these data eliminate a common source of measurement error in production function estimation. Plant-level productivity growth decomposition and the contribution of learning effect are explored by estimating the production frontier and firm-specific learning efficiency.Colombian food manufacturing industry, Bounded learning-by-doing, Learning efficiency, Logistic differential equation, Technical efficiency, Firm-level productivity growth, Decomposition of productivity change, Endogeneity of input choice, Stochastic production frontier, Agribusiness, Industrial Organization, Production Economics, Productivity Analysis,

Smart and efficient: Learning curves in manual and human-robot order picking systems

Order picking has been identified as the most labour-intensive, as well as costly activity within warehouse logistics and is experiencing significant changes due to new technologies in the forms of artificial intelligence (AI) and automation. One fundamental question concerns the employees learning progress in human-robot picking systems compared to existing manual technologies. Therefore, this paper presents an empirical analysis of learning curves in manual pick-by-voice (n=30 pickers) and semiautomated (n=20 pickers) order picking. Aspiring to measure the individual learning progress without a priori assumptions, this publication is the first to apply Data Envelopment Analysis and examine order pickers learning curves in real application scenarios. The findings indicate that automating human work accelerates the individual learning progress in human-robot picking systems

Building multi-layer social knowledge maps with google maps API

Google Maps is an intuitive online-map service which changes people's way of navigation on Geo-maps. People can explore the maps in a multi-layer fashion in order to avoid information overloading. This paper reports an innovative approach to extend the "power" of Google Maps to adaptive learning. We have designed and implemented a navigator for multi-layer social knowledge maps, namely ProgressiveZoom, with Google Maps API. In our demonstration, the knowledge maps are built from the Interactive System Design (ISD) course at the School of Information Science, University of Pittsburgh. Students can read the textbooks and reflect their individual and social learning progress in a context of pedagogical hierarchical structure