Software scaffolds to promote regulation during scientific inquiry learning

This research addresses issues in the design of online scaffolds for regulation within inquiry learning environments. The learning environment in this study included a physics simulation, data analysis tools, and a model editor for students to create runnable models. A regulative support tool called the Process Coordinator (PC) was designed to assist students in planning, monitoring, and evaluating their investigative efforts within this environment. In an empirical evaluation, 20 dyads received a “full” version of the PC with regulative assistance; dyads in the control group (n = 15) worked with an “empty” PC which contained minimal structures for regulative support. Results showed that both the frequency and duration of regulative tool use differed in favor of the PC+ dyads, who also wrote better lab reports. PC− dyads viewed the content helpfiles more often and produced better domain models. Implications of these differential effects are discussed and suggestions for future research are advanced

Personalised correction, feedback, and guidance in an automated tutoring system for skills training

In addition to knowledge, in various domains skills are equally important. Active learning and training are effective forms of education. We present an automated skills training system for a database programming environment that promotes procedural knowledge acquisition and skills training. The system provides support features such as correction of solutions, feedback and personalised guidance, similar to interactions with a human tutor. Specifically, we address synchronous feedback and guidance based on personalised assessment. Each of these features is automated and includes a level of personalisation and adaptation. At the core of the system is a pattern-based error classification and correction component that analyses student input

Psychometric Evaluation and Design of Patient-Centered Communication Measures for Cancer Care Settings

Objective To evaluate the psychometric properties of questions that assess patient perceptions of patient-provider communication and design measures of patient-centered communication (PCC). Methods Participants (adults with colon or rectal cancer living in North Carolina) completed a survey at 2 to 3 months post-diagnosis. The survey included 87 questions in six PCC Functions: Exchanging Information, Fostering Health Relationships, Making Decisions, Responding to Emotions, Enabling Patient Self-Management, and Managing Uncertainty. For each Function we conducted factor analyses, item response theory modeling, and tests for differential item functioning, and assessed reliability and construct validity. Results Participants included 501 respondents; 46% had a high school education or less. Reliability within each Function ranged from 0.90 to 0.96. The PCC-Ca-36 (36-question survey; reliability=0.94) and PCC-Ca-6 (6-question survey; reliability=0.92) measures differentiated between individuals with poor and good health (i.e., known-groups validity) and were highly correlated with the HINTS communication scale (i.e., convergent validity). Conclusion This study provides theory-grounded PCC measures found to be reliable and valid in colorectal cancer patients in North Carolina. Future work should evaluate measure validity over time and in other cancer populations. Practice implications The PCC-Ca-36 and PCC-Ca-6 measures may be used for surveillance, intervention research, and quality improvement initiatives

Generating and Adapting to Diverse Ad-Hoc Cooperation Agents in Hanabi

Hanabi is a cooperative game that brings the problem of modeling other players to the forefront. In this game, coordinated groups of players can leverage pre-established conventions to great effect, but playing in an ad-hoc setting requires agents to adapt to its partner's strategies with no previous coordination. Evaluating an agent in this setting requires a diverse population of potential partners, but so far, the behavioral diversity of agents has not been considered in a systematic way. This paper proposes Quality Diversity algorithms as a promising class of algorithms to generate diverse populations for this purpose, and generates a population of diverse Hanabi agents using MAP-Elites. We also postulate that agents can benefit from a diverse population during training and implement a simple "meta-strategy" for adapting to an agent's perceived behavioral niche. We show this meta-strategy can work better than generalist strategies even outside the population it was trained with if its partner's behavioral niche can be correctly inferred, but in practice a partner's behavior depends and interferes with the meta-agent's own behavior, suggesting an avenue for future research in characterizing another agent's behavior during gameplay.Comment: arXiv admin note: text overlap with arXiv:1907.0384

What learning analytics based prediction models tell us about feedback preferences of students

Learning analytics (LA) seeks to enhance learning processes through systematic measurements of learning related data and to provide informative feedback to learners and educators (Siemens & Long, 2011). This study examined the use of preferred feedback modes in students by using a dispositional learning analytics framework, combining learning disposition data with data extracted from digital systems. We analyzed the use of feedback of 1062 students taking an introductory mathematics and statistics course, enhanced with digital tools. Our findings indicated that compared with hints, fully worked-out solutions demonstrated a stronger effect on academic performance and acted as a better mediator between learning dispositions and academic performance. This study demonstrated how e-learners and their data can be effectively re-deployed to provide meaningful insights to both educators and learners

Modelling human teaching tactics and strategies for tutoring systems

One of the promises of ITSs and ILEs is that they will teach and assist learning in an intelligent manner. Historically this has tended to mean concentrating on the interface, on the representation of the domain and on the representation of the student’s knowledge. So systems have attempted to provide students with reifications both of what is to be learned and of the learning process, as well as optimally sequencing and adjusting activities, problems and feedback to best help them learn that domain. We now have embodied (and disembodied) teaching agents and computer-based peers, and the field demonstrates a much greater interest in metacognition and in collaborative activities and tools to support that collaboration. Nevertheless the issue of the teaching competence of ITSs and ILEs is still important, as well as the more specific question as to whether systems can and should mimic human teachers. Indeed increasing interest in embodied agents has thrown the spotlight back on how such agents should behave with respect to learners. In the mid 1980s Ohlsson and others offered critiques of ITSs and ILEs in terms of the limited range and adaptability of their teaching actions as compared to the wealth of tactics and strategies employed by human expert teachers. So are we in any better position in modelling teaching than we were in the 80s? Are these criticisms still as valid today as they were then? This paper reviews progress in understanding certain aspects of human expert teaching and in developing tutoring systems that implement those human teaching strategies and tactics. It concentrates particularly on how systems have dealt with student answers and how they have dealt with motivational issues, referring particularly to work carried out at Sussex: for example, on responding effectively to the student’s motivational state, on contingent and Vygotskian inspired teaching strategies and on the plausibility problem. This latter is concerned with whether tactics that are effectively applied by human teachers can be as effective when embodied in machine teachers

Automated tutoring for a database skills training environment

Universities are increasingly offering courses online. Feedback, assessment, and guidance are important features of this online courseware. Together, in the absence of a human tutor, they aid the student in the learning process. We present a programming training environment for a database course. It aims to offer a substitute for classroom based learning by providing synchronous automated feedback to the student, along with guidance based on a personalized assessment. The automated tutoring system should promote procedural knowledge acquisition and skills training. An automated tutoring feature is an integral part of this tutoring system

A Collaborative Lecture in Information Retrieval for Students at Universities in Germany and Switzerland

K3, work in progress, is an acronym for Kollaboration (collaboration), Kommunikation (communication), and Kompetenz (competence). K3 provides a platform in the context of knowledge management to support collaborative knowledge production in learning environments. The underlying hypothesis states that collaborative discourse conciliates information as well as communication competence in learning contexts. The collaborative, communicative paradigm of K3 is implemented by asynchronous communication tools as a means of constructivist learning methodology. In this paper we will describe a K3 course. The lecture was organized and carried out at two places in two different countries (Germany and Switzerland) with students from different universities in the context of Library and Information Science. The paper informs about the management of the lecture and about the problems we had to run the lecture at two places. The circumstances in coordinating the presentations, the exercises, the examinations and evaluation, and the time schedule are presented. The conclusions of the lecturers and the results of a questionnaire for the students are explained in detail