Supporting Constructive Learning with a Feedback Planner

A promising approach to constructing more effective computer tutors is implementing tutorial strategies that extend over multiple turns. This means that computer tutors must deal with (1) failure, (2) interruptions, (3) the need to revise their tactics, and (4) basic dialogue phenomena such as acknowledgment. To deal with these issues, we need to combine ITS technology with advances from robotics and computational linguistics. We can use reactive planning techniques from robotics to allow us to modify tutorial plans, adapting them to student input. Computational linguistics will give us guidance in handling communication management as well as building a reusable architecture for tutorial dialogue systems. A modular and reusable architecture is critical given the difficulty in constructing tutorial dialogue systems and the many domains to which we would like to apply them. In this paper, we propose such an architecture and discuss how a reactive planner in the context of this architecture can implement multi-turn tutorial strategies

Learning About Something Means Becoming Wiser: The Platonic Dialogue as a Paradigmatic Model for Writing Center Practice

As our discipline’s scholars, we must recognize that ours is a history “that is best recognized as an always incomplete narrative” and continue to delve into the past as we seek to inform our future (Lerner 25). In this article, I delve into Plato’s use of “elenchus” or cross-questioning for the purpose of achieving “aporia”—the sense of perplexity or confusion that usually accompanies the discovery that language does not have the ability to mean in any stable sense” within Theaetetus (Raign 90). In addition to extending our narrative history, studying the process of elenchus will allow us to share this methodology with our tutors, so that they can develop the ability not to merely engage in conversation with their students, or lead them to a truth not their own, but engage in the type of inquiry about language and its ability to mean that leads students toward the sort of self-discovery present in the Platonic dialogues.University Writing Cente

Modelling benefits-oriented costs for technology enhanced learning

The introduction of technology enhanced learning (TEL) methods changes the deployment of the most important resource in the education system: teachers' and learners' time. New technology promises greater personalization and greater productivity, but without careful modeling of the effects on the use of staff time, TEL methods can easily increase cost without commensurate benefit. The paper examines different approaches to comparing the teaching time costs of TEL with traditional methods, concluding that within-institution cost-benefit modeling yields the most accurate way of understanding how teachers can use the technology to achieve the level of productivity that makes personalisation affordable. The analysis is used to generate a set of requirements for a prospective, rather than retrospective cost-benefit model. It begins with planning decisions focused on realizing the benefits of TEL, and uses these to derive the likely critical costs, hence the reversal implied by a 'benefits-oriented cost model'. One of its principal advantages is that it enables innovators to plan and understand the relationship between the expected learning benefits and the likely teaching costs

Perceptions from Graduates of Professional Athletic Training Programs Involved in Peer-Assisted Learning

Context: Research has not explored how peer-assisted learning (PAL) impacts graduates once they are practicing as athletic trainers. Peer-assisted learning has been used in a variety of health education settings but there is a lack of data on its effects on the performance of graduates. Objective: To investigate professional graduates’ perceptions of PAL pedagogy in their athletic training education and the impact of that experience on their first job. Design: Qualitative study using a phenomenological approach. Setting: One-on-one phone interviews with athletic training graduates. Patients or Other Participants: Participants were from 8 accredited athletic training programs that varied in terms of the size of their institution, geographic location, number of graduates, and program directors’ willingness to promote the study. Thirteen (7 female, 6 male) 2010 (n ¼ 5) or 2011 (n ¼ 8) graduates volunteered for this study. Ten of the participants were from undergraduate baccalaureate athletic training programs, while 3 were from professional postbaccalaureate athletic training programs. Main Outcome Measure(s): One-on-one phone interviews were conducted with a structured interview protocol. Each participant was asked the same questions and allowed to clarify when needed. Interview data were analyzed inductively to uncover dominant themes, first by organizing the data, then by summarizing them into codes, and finally by interpreting them. Credibility was secured through a pilot study, member checking, triangulation, and peer debriefing. Results: Data were analyzed through a qualitative process; themes indicated graduates who have experienced PAL believe it led to improved communication and confidence, enhanced teaching skills, better clinical reasoning, improved socialization, and a deeper understanding that contributed to success on the Board of Certification examination. Conclusions: These findings are significant to the field of athletic training education as program directors investigate pedagogies that can assist students to think clinically as graduates. Evidence demonstrated that PAL does impact the students after graduation

Critical factors influencing in service teachers’ satisfaction with an e-learning course, measured with a structural equation modelling

This research aimed at assessing the perceptions of 168 K 12 teachers about a b-learning course on ICT integration in the curriculum. The teacher trainees were asked to plan some class activities, using Web 2.0 technologies; they have also discussed several issues related to education in the knowledge society, such as new ways and strategies for infusing ICT in the national curricula. A learning environment supported by a communication platform was designed. At the end of the course, a satisfaction survey, which was the basis for this research, was applied to assess the different dimensions of the course. Variables such as ICT skills, course design, collaboration, instructor’s feedback, course usefulness and learners’ satisfaction were assessed. The majority of the trainees (83.3%) affirmed that, in the future, would easily choose a b-learning modality course. Three face-toface sessions – at the beginning, in the middle and at the end of the course - have proved to be enough to provide support in technological issues, to organize teamwork, to develop stronger relationships within the community and to keep the trainees on track. The results indicate positive perceptions about the online learning environment and strong relationships with both endogenous and exogenous variables. The trainees pointed out, both in their personal reports and in the survey, that collaboration was one of the most valued components of the course. In fact, the trainees were provided a collaboration board where everyone could ask questions or for some kind of help with the web tools used in the course. The research will develop by using a larger and more diverse students’ sample and by surveying the tutors´ perspectives on eLearning, in order to provide the most convenient learning methodologies

Logistic Knowledge Tracing: A Constrained Framework for Learner Modeling

Adaptive learning technology solutions often use a learner model to trace learning and make pedagogical decisions. The present research introduces a formalized methodology for specifying learner models, Logistic Knowledge Tracing (LKT), that consolidates many extant learner modeling methods. The strength of LKT is the specification of a symbolic notation system for alternative logistic regression models that is powerful enough to specify many extant models in the literature and many new models. To demonstrate the generality of LKT, we fit 12 models, some variants of well-known models and some newly devised, to 6 learning technology datasets. The results indicated that no single learner model was best in all cases, further justifying a broad approach that considers multiple learner model features and the learning context. The models presented here avoid student-level fixed parameters to increase generalizability. We also introduce features to stand in for these intercepts. We argue that to be maximally applicable, a learner model needs to adapt to student differences, rather than needing to be pre-parameterized with the level of each student's ability