Pattern separation in digital learning nets

Pattern separation is a new technique in digital learning networks which can be used to detect state conflicts. This letter describes pattern separation in a simple single-layer network, and an application of the technique in networks with feedback.

Once upon a time, there was a pulchritudinous princess…: the role of word definitions and multiple story contexts in children’s learning of difficult vocabulary

The close relationship between children’s vocabulary size and their later academic success has led researchers to explore how vocabulary development might be promoted during the early school years. We describe a study that explored the effectiveness of naturalistic classroom storytelling as an instrument for teaching new vocabulary to six- to nine-year-old children. We examined whether learning was facilitated by encountering new words in single versus multiple story contexts, or by the provision of age-appropriate definitions of words as they were encountered. Results showed that encountering words in stories on three occasions led to significant gains in word knowledge in children of all ages and abilities, and that learning was further enhanced across the board when teachers elaborated on the new words’ meanings by providing dictionary definitions. Our findings clarify how classroom storytelling activities can be a highly effective means of promoting vocabulary development.

Evolutionary model trees for handling continuous classes in machine learning

Model trees are a particular case of decision trees employed to solve regression problems. They have the advantage of presenting an interpretable output, helping the end-user to get more confidence in the prediction and providing the basis for the end-user to have new insight about the data, confirming or rejecting hypotheses previously formed. Moreover, model trees present an acceptable level of predictive performance in comparison to most techniques used for solving regression problems. Since generating the optimal model tree is an NP-Complete problem, traditional model tree induction algorithms make use of a greedy top-down divide-and-conquer strategy, which may not converge to the global optimal solution. In this paper, we propose a novel algorithm based on the use of the evolutionary algorithms paradigm as an alternate heuristic to generate model trees in order to improve the convergence to globally near-optimal solutions. We call our new approach evolutionary model tree induction (E-Motion). We test its predictive performance using public UCI data sets, and we compare the results to traditional greedy regression/model trees induction algorithms, as well as to other evolutionary approaches. Results show that our method presents a good trade-off between predictive performance and model comprehensibility, which may be crucial in many machine learning applications. (C) 2010 Elsevier Inc. All rights reserved.

Students’ Role in Distance Learning

1. IntroductionMuch of the support that students have in a traditional classroom is absent in a distance learning course. In the traditional classroom, the learner is together with his or her classmates and the teacher; learning is socially embedded. Students can talk to each other and may learn from each other as they go through the learning process together. They also witness the teacher’s expression of the knowledge firsthand. The class participants communicate to each other not only through their words, but also through their gestures, facial expressions and tone of voice, and the teacher can observe the students’ progress and provide guidance and feedback in an as-needed basis. Further, through the habit of meeting in a regular place at a regular time, the participants reinforce their own and each other’s commitment to the course. A distance course must somehow provide learners other kinds of supports so that the distance learner also has a sense of connection with a learning community; can benefit from interaction with peers who are going through a similar learning process; receives feedback that allows him or her to know how he or she is progressing; and is guided enough so that he or she continues to progress towards the learning objectives. This cannot be accomplished if the distance course does not simultaneously promote student autonomy, for the distance course format requires students to take greater responsibility for their own learning. This chapter presents one distance learning course that was able to address all of these goals. The English Department at Högskolan Dalarna, Sweden, participates in a distance learning program with Vietnam National University. Students enrolled in this program study half-time for two years to complete a Master’s degree in English Linguistics. The distance courses in this program all contain two types of regular class meetings: one type is student-only seminars conducted through text chat, during which students discuss and complete assignments that prepare them for the other type of class meeting, also conducted through text chat, where the teacher is present and is the one to lead the discussion of seminar issues and assignments. The inclusion of student-only seminars in the course design allows for student independence while at the same time it encourages co-operation and solidarity. The teacher-led seminars offer the advantages of a class led by an expert.In this chapter, we present chatlog data from Vietnamese students in one distance course in English linguistics, comparing the role of the student in both student-only and teacher-led seminars. We discuss how students navigate their participation roles, through computer-mediated communication (CMC), according to seminar type, and we consider the emerging role of the autonomous student in the foreign-language medium, distance learning environment. We close by considering aspects of effective design of distance learning courses from the perspective of a foreign language (FL) environment.

Languaging in virtual learning sites : studies of online encounters in the language-focused classroom

This thesis focuses upon a series of empirical studies which examine communication and learning in online glocal communities within higher education in Sweden. A recurring theme in the theoretical framework deals with issues of languaging in virtual multimodal environments as well as the making of identity and negotiation of meaning in these settings; analyzing the activity, what people do, in contraposition to the study of how people talk about their activity. The studies arise from netnographic work during two online Italian for Beginners courses offered by a Swedish university. Microanalyses of the interactions occurring through multimodal video-conferencing software are amplified by the study of the courses’ organisation of space and time and have allowed for the identification of communicative strategies and interactional patterns in virtual learning sites when participants communicate in a language variety with which they have a limited experience. The findings from the four studies included in the thesis indicate that students who are part of institutional virtual higher educational settings make use of several resources in order to perform their identity positions inside the group as a way to enrich and nurture the process of communication and learning in this online glocal community. The sociocultural dialogical analyses also shed light on the ways in which participants gathering in discursive technological spaces benefit from the opportunity to go to class without commuting to the physical building of the institution providing the course. This identity position is, thus, both experienced by participants in interaction, and also afforded by the ‘spaceless’ nature of the online environment.

Facilitation of learning in specialist nursing training in the PICU : the supervisors´ concerns in the learning situation

With the aim to unfold nurses’ concerns of the supervision of the student in the clinical caring situation of the vulnerable child, clinical nurses situated supervision of postgraduate nursing students in the Pediatric Intensive Care Unit (PICU) are explored. A qualitative approach, interpretive phenomenology, with participant observations and narrative interviews, was used. Two qualitative variations of patterns of meaning for the nurses’ clinical facilitation were disclosed in this study. Learning by doing theme supports the students learning by doing through performing skills and embracing routines. The reflecting theme supports thinking and awareness of the situation. As the supervisor often serves as a role model for the student this might have an immediate impact on how the student applies nursing care in the beginning of his or her career. If the clinical supervisor narrows the perspective and hinders room for learning the student will bring less knowledge from the clinical education than expected, which might result in reduced nursing quality.

Power-law distribution in a learning process: competition, learning and natural selection

In the present work, we propose a model for the statistical distribution of people versus number of steps acquired by them in a learning process, based on competition, learning and natural selection. We consider that learning ability is normally distributed. We found that the number of people versus step acquired by them in a learning process is given through a power law. As competition, learning and selection is also at the core of all economical and social systems, we consider that power-law scaling is a quantitative description of this process in social systems. This gives an alternative thinking in holistic properties of complex systems. (C) 2004 Elsevier B.V. All rights reserved.

Using virtual worlds in distance learning environments

This paper presents two tools developed to facilitate the use and automate the process of using Virtual Worlds for educational purposes. The first tool has been developed to automatically create the classroom space, usually called region in the virtual world, which means, a region in the virtual world used to develop educational activities between professors, students and interactive objects. The second tool helps the process of creating 3D interactive objects in a virtual world. With these tools educators will be able to produce 3D interactive learning objects and use them in virtual classrooms improving the quality and appeal, for students, of their classes. © 2011 IEEE.

Rich media content adaptation in e-learning systems

The wide use of e-technologies represents a great opportunity for underserved segments of the population, especially with the aim of reintegrating excluded individuals back into society through education. This is particularly true for people with different types of disabilities who may have difficulties while attending traditional on-site learning programs that are typically based on printed learning resources. The creation and provision of accessible e-learning contents may therefore become a key factor in enabling people with different access needs to enjoy quality learning experiences and services. Another e-learning challenge is represented by m-learning (which stands for mobile learning), which is emerging as a consequence of mobile terminals diffusion and provides the opportunity to browse didactical materials everywhere, outside places that are traditionally devoted to education. Both such situations share the need to access materials in limited conditions and collide with the growing use of rich media in didactical contents, which are designed to be enjoyed without any restriction. Nowadays, Web-based teaching makes great use of multimedia technologies, ranging from Flash animations to prerecorded video-lectures. Rich media in e-learning can offer significant potential in enhancing the learning environment, through helping to increase access to education, enhance the learning experience and support multiple learning styles. Moreover, they can often be used to improve the structure of Web-based courses. These highly variegated and structured contents may significantly improve the quality and the effectiveness of educational activities for learners. For example, rich media contents allow us to describe complex concepts and process flows. Audio and video elements may be utilized to add a “human touch” to distance-learning courses. Finally, real lectures may be recorded and distributed to integrate or enrich on line materials. A confirmation of the advantages of these approaches can be seen in the exponential growth of video-lecture availability on the net, due to the ease of recording and delivering activities which take place in a traditional classroom. Furthermore, the wide use of assistive technologies for learners with disabilities injects new life into e-learning systems. E-learning allows distance and flexible educational activities, thus helping disabled learners to access resources which would otherwise present significant barriers for them. For instance, students with visual impairments have difficulties in reading traditional visual materials, deaf learners have trouble in following traditional (spoken) lectures, people with motion disabilities have problems in attending on-site programs. As already mentioned, the use of wireless technologies and pervasive computing may really enhance the educational learner experience by offering mobile e-learning services that can be accessed by handheld devices. This new paradigm of educational content distribution maximizes the benefits for learners since it enables users to overcome constraints imposed by the surrounding environment. While certainly helpful for users without disabilities, we believe that the use of newmobile technologies may also become a fundamental tool for impaired learners, since it frees them from sitting in front of a PC. In this way, educational activities can be enjoyed by all the users, without hindrance, thus increasing the social inclusion of non-typical learners. While the provision of fully accessible and portable video-lectures may be extremely useful for students, it is widely recognized that structuring and managing rich media contents for mobile learning services are complex and expensive tasks. Indeed, major difficulties originate from the basic need to provide a textual equivalent for each media resource composing a rich media Learning Object (LO). Moreover, tests need to be carried out to establish whether a given LO is fully accessible to all kinds of learners. Unfortunately, both these tasks are truly time-consuming processes, depending on the type of contents the teacher is writing and on the authoring tool he/she is using. Due to these difficulties, online LOs are often distributed as partially accessible or totally inaccessible content. Bearing this in mind, this thesis aims to discuss the key issues of a system we have developed to deliver accessible, customized or nomadic learning experiences to learners with different access needs and skills. To reduce the risk of excluding users with particular access capabilities, our system exploits Learning Objects (LOs) which are dynamically adapted and transcoded based on the specific needs of non-typical users and on the barriers that they can encounter in the environment. The basic idea is to dynamically adapt contents, by selecting them from a set of media resources packaged in SCORM-compliant LOs and stored in a self-adapting format. The system schedules and orchestrates a set of transcoding processes based on specific learner needs, so as to produce a customized LO that can be fully enjoyed by any (impaired or mobile) student.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior, and in particular in behavioral decision making. Such decision making is likely to involve the integration of many synaptic events in space and time. However, using a single reinforcement signal to modulate synaptic plasticity, as suggested in classical reinforcement learning algorithms, a twofold problem arises. Different synapses will have contributed differently to the behavioral decision, and even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike-time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward, but also by a population feedback signal. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference (TD) based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task, the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second task involves an action sequence which is itself extended in time and reward is only delivered at the last action, as it is the case in any type of board-game. The third task is the inspection game that has been studied in neuroeconomics, where an inspector tries to prevent a worker from shirking. Applying our algorithm to this game yields a learning behavior which is consistent with behavioral data from humans and monkeys, revealing themselves properties of a mixed Nash equilibrium. The examples show that our neuronal implementation of reward based learning copes with delayed and stochastic reward delivery, and also with the learning of mixed strategies in two-opponent games.

Spatio-temporal credit assignment in population learning

Learning by reinforcement is important in shaping animal behavior. But behavioral decision making is likely to involve the integration of many synaptic events in space and time. So in using a single reinforcement signal to modulate synaptic plasticity a twofold problem arises. Different synapses will have contributed differently to the behavioral decision and, even for one and the same synapse, releases at different times may have had different effects. Here we present a plasticity rule which solves this spatio-temporal credit assignment problem in a population of spiking neurons. The learning rule is spike time dependent and maximizes the expected reward by following its stochastic gradient. Synaptic plasticity is modulated not only by the reward but by a population feedback signal as well. While this additional signal solves the spatial component of the problem, the temporal one is solved by means of synaptic eligibility traces. In contrast to temporal difference based approaches to reinforcement learning, our rule is explicit with regard to the assumed biophysical mechanisms. Neurotransmitter concentrations determine plasticity and learning occurs fully online. Further, it works even if the task to be learned is non-Markovian, i.e. when reinforcement is not determined by the current state of the system but may also depend on past events. The performance of the model is assessed by studying three non-Markovian tasks. In the first task the reward is delayed beyond the last action with non-related stimuli and actions appearing in between. The second one involves an action sequence which is itself extended in time and reward is only delivered at the last action, as is the case in any type of board-game. The third is the inspection game that has been studied in neuroeconomics. It only has a mixed Nash equilibrium and exemplifies that the model also copes with stochastic reward delivery and the learning of mixed strategies.