IRT-Based Adaptive Hints to Scaffold Learning in Programming

Over the past few decades, many studies conducted in the field of learning science have described that scaffolding plays an important role in human learning. To scaffold a learner efficiently, a teacher should predict how much support a learner must have to complete tasks and then decide the optimal degree of assistance to support the learner\u27s development. Nevertheless, it is difficult to ascertain the optimal degree of assistance for learner development. For this study, it is assumed that optimal scaffolding is based on a probabilistic decision rule: Given a teacher\u27s assistance to facilitate the learner development, an optimal probability exists for a learner to solve a task. To ascertain that optimal probability, we developed a scaffolding system that provides adaptive hints to adjust the predictive probability of the learner\u27s successful performance to the previously determined certain value, using a probabilistic model, i.e., item response theory (IRT). Furthermore, using the scaffolding system, we compared learning performances by changing the predictive probability. Results show that scaffolding to achieve 0.5 learner success probability provides the best performance. Additionally, results demonstrate that a scaffolding system providing 0.5 probability decreases the number of hints (amount of support) automatically as a fading function according to the learner\u27s growth capability

An ensemble-based computational approach for incremental learning in non-stationary environments related to schema- and scaffolding-based human learning

The principal dilemma in a learning process, whether human or computer, is adapting to new information, especially in cases where this new information conflicts with what was previously learned. The design of computer models for incremental learning is an emerging topic for classification and prediction of large-scale data streams undergoing change in underlying class distributions (definitions) over time; yet currently, they often ignore significant foundational learning theory that has been developed in the domain of human learning. This shortfall leads to many deficiencies in the ability to organize existing knowledge and to retain relevant knowledge for long periods of time. In this work, we introduce a unique computer-learning algorithm for incremental knowledge acquisition using an ensemble of classifiers, Learn++.NSE (Non-Stationary Environments), specifically for the case where the nature of knowledge to be learned is evolving. Learn++.NSE is a novel approach to evaluating and organizing existing knowledge (classifiers) according to the most recent data environment. Under this architecture, we address the learning problem at both the learner and supervisor end, discussing and implementing three main approaches: knowledge weighting/organization, forgetting prior knowledge, and change/drift detection. The framework is evaluated on a variety of canonical and real-world data streams (weather prediction, electricity price prediction, and spam detection). This study reveals the catastrophic effect of forgetting prior knowledge, supporting the organization technique proposed by Learn++.NSE as the most consistent performer during various drift scenarios, while also addressing the sheer difficulty in designing a system that strikes a balance between maintaining all knowledge and making decisions based only on relevant knowledge, especially in severe, unpredictable environments which are often encountered in the real-world

Adults are more efficient in creating and transmitting novel signalling systems than children

Iterated language learning experiments have shown that meaningful and structured signalling systems emerge when there is pressure for signals to be both learnable and expressive. Yet such experiments have mainly been conducted with adults using language-like signals. Here we explore whether structured signalling systems can also emerge when signalling domains are unfamiliar and when the learners are children with their well-attested cognitive and pragmatic limitations. In Experiment 1, we compared iterated learning of binary auditory sequences denoting small sets of meanings in chains of adults and 5-7-year old children. Signalling systems became more learnable even though iconicity and structure did not emerge despite applying a homonymy filter designed to keep the systems expressive. When the same types of signals were used in referential communication by adult and child dyads in Experiment 2, only the adults, but not the children, were able to negotiate shared iconic and structured signals. Referential communication using their native language by 4-5-year old children in Experiment 3 showed that only interaction with adults, but not with peers resulted in informative expressions. These findings suggest that emergence and transmission of communication systems is unlikely to be driven by children, and point to the importance of cognitive maturity and pragmatic expertise of learners as well as feedback-based scaffolding of communicative effectiveness by experts during language evolution

Scaffolding for Optimal Challenge in K–12 Problem-Based Learning

Establishing optimal challenge enhances intrinsic motivation, interest, and the probability of success in the learning activity. In K–12 problem-based learning (PBL), students may struggle to address associated tasks that are beyond their current ability levels. This paper suggested learner-centered scaffolding systems (LSS) to improve K–12 students’ perception of optimal challenge by addressing their learning issues in PBL. LSS enhances students’ experience in autonomy and competence by providing multiple types of scaffolding in accordance with students’ different needs and difficulties in PBL. Students can control the nature and frequency of scaffolding by themselves according to their needs and ability, and it plays a role in improving their self-directed learning skills. Last, peer scaffolding between students with similar abilities satisfies students’ needs for relatedness

The impact of model-lead-test coaching on parents\u27 implementation of reinforcement, prompting, and fading with their children with autism spectrum disorder

Parents play an essential role in furthering the development of their children with special needs. They are being trained to be co-therapists for their own children. The goal is to improve the ways they interact with their children in order to create improvements in their children\u27s everyday functioning. If the proper teaching strategies are consistently applied, a learner can significantly improve his/her performance of various life skills, including communication, self-care, social skills, along with other skill sets. Because adults\u27 learning processes differ substantially from children\u27s, it will be critical to utilize the coaching method that employs a Model-Lead-Test (MLT) approach to effectively train parents of children with autism. The primary purpose of this study is to evaluate the impact of model-lead-test coaching on parents\u27 use of prompting, fading, and reinforcement with their children with Autism Spectrum Disorder (ASD). The secondary purpose is to assess whether there are improvements in the children\u27s talker, participator, and problem solver repertoires associated with their parents\u27 use of these behavior change processes.;Using a multiple baseline across behavior design for each parent-child dyad, data are collected on parents\u27 proper use of reinforcement, prompting, and fading as well as their children\u27s talker, participator, and problem solver repertoire development. Research phases include baseline, parent training I (Oral Lecture), parent training II (Model-Lead-Test), and maintenance.;The resulting data from this study indicate that the Model-Lead-Test approach to parent implementation of core ABA strategies has a greater impact than merely using an Oral Lecture Discussion approach to parent training. To summarize, the data from all three participants showed an increase in the proper implementation of reinforcement, prompting and fading procedures especially through MLT training. The child participants also showed an increase in their talker, participator and problem solver repertoires. And finally, interpretation of the data is presented along with possible future guidelines for research

Technology Enabled Assessments: An Investigation of Scoring Models for Scaffolded Tasks

While significant progress has been made in recent years on technology enabled assessments (TEAs), including assessment systems that incorporate scaffolding into the assessment process, there is a dearth of research regarding psychometric scoring models that can be used to fully capture students' knowledge, skills and abilities as measured by TEAs. This investigation provides a comparison of seven scoring models applied to an operational assessment system that incorporates scaffolding into the assessment process and evaluates student ability estimates derived from those models from a validity perspective. A sequential procedure for fitting and evaluating increasingly complex models was conducted. Specifically, a baseline model that did not account for any scaffolding features in the assessment system was established and compared to three additional models that each accounted for scaffolding features using a dichotomous, a polytomous and a testlet model approach. Models were compared and evaluated against several criteria including model convergence, the amount of information each model provided and the statistical relationships between scaled scores and a criterion measure of student ability. Based on these criteria, the dichotomous model that accounted for all of the scaffold items but ignored local dependence was determined to be the optimal scoring model for the assessment system used in this study. However, if the violation against the local independence assumption is deemed unacceptable, it was also concluded that the polytomous model for scoring these assessments is a worthwhile and viable alternative. In any case, the scoring models that accounted for the scaffolding features in the assessment system were determined to be better overall models than the baseline model that did not account for these features. It was also determined that the testlet model approach was not a practical or useful scoring option for this assessment system. Given the purpose of the assessment system used in this study, which is a formative tool that also provides instructional opportunities to students during the assessment process, the advantages of applying any of these scoring models from a measurement perspective may not justify the practical disadvantages. For instance, a basic percent correct score may be completely dependent on the specific items that a student took but it is relatively simple to understand and compute. On the other hand, scaled scores from these scoring models are independent of the items from which they were calibrated from, but ability estimates are more complex to understand and derive. As the assessment system used in this study is a low stakes environment that is mostly geared towards learning, the benefits of the scoring models presented in this study need to be weighed against the practical constraints within an operational context with respect to time, cost and resources

Structured computer-based training in the interpretation of neuroradiological images

Computer-based systems may be able to address a recognised need throughout the medical profession for a more structured approach to training. We describe a combined training system for neuroradiology, the MR Tutor that differs from previous approaches to computer-assisted training in radiology in that it provides case-based tuition whereby the system and user communicate in terms of a well-founded Image Description Language. The system implements a novel method of visualisation and interaction with a library of fully described cases utilising statistical models of similarity, typicality and disease categorisation of cases. We describe the rationale, knowledge representation and design of the system, and provide a formative evaluation of its usability and effectiveness