Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Personalized Approaches to Supporting the Learning Needs of Lifelong Professional Learners

Advanced learning technology research has begun to take on a complex challenge: supporting lifelong learning. Professional learning is an essential subset of lifelong learning that is more tractable than the full lifelong learning challenge. Professionals do not always have access to professional teachers to provide input to the problems they encounter, so they rely on their peers in an online learning community (OLC) to help meet their learning needs. Supporting professional learners within an OLC is a difficult problem as the learning needs of each learner continuously evolve, often in different ways from other learners. Hence, there is a need to provide personalized support to learners adapted to their individual learning needs. This thesis explores personalized approaches for detecting the unperceived learning needs and meeting the expressed learning needs of learners in an OLC. The experimental test bed for this research is Stack Overflow (SO), an OLC used by software professionals. To date, seven experiments have been carried out mining SO peer-peer interaction data. Knowing that question-answerers play a huge role in meeting the learning needs of the question-askers, the first experiment aimed to detect the learning needs of the answerers. Results from experiment 1 show that reputable answerers themselves demonstrate unperceived learning needs as revealed by a decline in quality answers in SO. Of course, a decline in quality answers could impact the help-seeking experience of question-askers; hence experiment 2 sought to understand the effects of the help-seeking experience of question-askers on their enthusiasm to continuously participate within the OLC. As expected, negative help-seeking experiences of question-askers had a large impact on their propensity to seek further help within the OLC. To improve the help-seeking experience of question-askers, it is important to proactively detect the learning needs of the question-answerers before they provide poor quality answers. Thus, in experiment 3 the goal was to predict whether a question-answerer would give a poor answer to a question based on their past peer-peer interactions. Under various assumptions, accuracies ranging from 84.57% to 94.54% were achieved. Next, experiment 4 attempted to detect the unperceived learning needs of question-askers even before they are aware of such needs. Using information about a learner’s interactions over a 5-month period, a prediction was made as to what they would be asking about during the next month, achieving recall and precision values of 0.93 and 0.81. Knowing the learning needs of question-askers early creates an opportunity to predict prospective answerers who could provide timely and quality answers to their question. The goal of experiment 5 was thus to predict the actual answerers for questions based only on information known at the time the question was asked. The iv success rate was at best 63.15%, which would only be marginally useful to inform a real-life peer recommender system. Thus, experiment 6 explored new measures in predicting the answerers, boosting the success rate to 89.64%. Of course, a peer recommender system would be deemed to be especially useful if it can provide prompt interventions, especially to get answers to questions that would otherwise not be answered quickly. To this end, experiment 7 attempted to predict the question-askers whose questions would be answered late or even remain unanswered, and a success rate of 68.4% was achieved. Results from these experiments suggest that modelling the activities of learners in an OLC is key in providing support to them to meet their learning needs. Perhaps, the most important lesson learned in this research is that lightweight approaches can be developed to help meet the evolving learning needs of professionals, even as knowledge changes within a profession. Metrics based on the experiments above are exactly such lightweight methodologies and could be the basis for useful tools to support professional learners

Towards Personalized Learning using Counterfactual Inference for Randomized Controlled Trials

Personalized learning considers that the causal effects of a studied learning intervention may differ for the individual student (e.g., maybe girls do better with video hints while boys do better with text hints). To evaluate a learning intervention inside ASSISTments, we run a randomized control trial (RCT) by randomly assigning students into either a control condition or a treatment condition. Making the inference about causal effects of studies interventions is a central problem. Counterfactual inference answers “What if� questions, such as Would this particular student benefit more if the student were given the video hint instead of the text hint when the student cannot solve a problem? . Counterfactual prediction provides a way to estimate the individual treatment effects and helps us to assign the students to a learning intervention which leads to a better learning. A variant of Michael Jordan\u27s Residual Transfer Networks was proposed for the counterfactual inference. The model first uses feed-forward neural networks to learn a balancing representation of students by minimizing the distance between the distributions of the control and the treated populations, and then adopts a residual block to estimate the individual treatment effect. Students in the RCT usually have done a number of problems prior to participating it. Each student has a sequence of actions (performance sequence). We proposed a pipeline to use the performance sequence to improve the performance of counterfactual inference. Since deep learning has achieved a huge amount of success in learning representations from raw logged data, student representations were learned by applying the sequence autoencoder to performance sequences. Then, incorporate these representations into the model for counterfactual inference. Empirical results showed that the representations learned from the sequence autoencoder improved the performance of counterfactual inference

Enriching e-learning metadata through digital library usage analysis

Purpose: In this paper we propose an evaluation framework for analyzing learning objects usage, with the aim of extracting useful information for improving the quality of the metadata used to describe the learning objects, but also for personalization purposes, including user models and adaptive itineraries. Methodology: We present experimental results from the log usage analysis during one academic semester of two different subjects, 350 students. The experiment looks into raw server log data generated from the interactions of the students with the classroom learning objects, in order to find relevant information that can be used to improve the metadata used for describing both the learning objects and the learning process. Findings: Preliminary studies have been carried out in order to obtain an initial picture of the interactions between learners and the virtual campus, including both services and resources usage. These studies try to establish elationships between user profiles and their information and navigational behavior in the virtual campus, with the aim of promoting personalization and improving the understanding of what learning in virtual environments means. Research limitations: During the formal learning process, students use learning resources from the virtual classroom provided by the academic library, but they also search for information outside the virtual campus. Not all of these usage data are considered in the model we propose. Further research needs to be done in order to get a complete view of the information search behavior of students for improving the users’ profile and creating better personalized services. Practical implications: In this paper we suggest how a selection of fields used in the LOM standard could be used for enriching the description of learning objects, automatically in some cases, from the learning objects usage performed by an academic community. Originality: Ever since the beginnings of libraries, they have been a “quiet storage place”. With the development of digital libraries, they become a meeting place where explicit and implicit recommendations about information sources can be shared among users. Social and learning process interactions, therefore, can be considered another knowledge source

Considering temporal aspects in recommender systems: a survey

Under embargo until: 2023-07-04The widespread use of temporal aspects in user modeling indicates their importance, and their consideration showed to be highly effective in various domains related to user modeling, especially in recommender systems. Still, past and ongoing research, spread over several decades, provided multiple ad-hoc solutions, but no common understanding of the issue. There is no standardization and there is often little commonality in considering temporal aspects in different applications. This may ultimately lead to the problem that application developers define ad-hoc solutions for their problems at hand, sometimes missing or neglecting aspects that proved to be effective in similar cases. Therefore, a comprehensive survey of the consideration of temporal aspects in recommender systems is required. In this work, we provide an overview of various time-related aspects, categorize existing research, present a temporal abstraction and point to gaps that require future research. We anticipate this survey will become a reference point for researchers and practitioners alike when considering the potential application of temporal aspects in their personalized applications.acceptedVersio

Balanced difficulty task finder: an adaptive recommendation method for learning tasks based on the concept of state of flow

publishedVersio

Interaction design guidelines on critiquing-based recommender systems

A critiquing-based recommender system acts like an artificial salesperson. It engages users in a conversational dialog where users can provide feedback in the form of critiques to the sample items that were shown to them. The feedback, in turn, enables the system to refine its understanding of the user's preferences and prediction of what the user truly wants. The system is then able to recommend products that may better stimulate the user's interest in the next interaction cycle. In this paper, we report our extensive investigation of comparing various approaches in devising critiquing opportunities designed in these recommender systems. More specifically, we have investigated two major design elements which are necessary for a critiquing-based recommender system: critiquing coverage—one vs. multiple items that are returned during each recommendation cycle to be critiqued; and critiquing aid—system-suggested critiques (i.e., a set of critique suggestions for users to select) vs. user-initiated critiquing facility (i.e., facilitating users to create critiques on their own). Through a series of three user trials, we have measured how real-users reacted to systems with varied setups of the two elements. In particular, it was found that giving users the choice of critiquing one of multiple items (as opposed to just one) has significantly positive impacts on increasing users' decision accuracy (particularly in the first recommendation cycle) and saving their objective effort (in the later critiquing cycles). As for critiquing aids, the hybrid design with both system-suggested critiques and user-initiated critiquing support exhibits the best performance in inspiring users' decision confidence and increasing their intention to return, in comparison with the uncombined exclusive approaches. Therefore, the results from our studies shed light on the design guidelines for determining the sweetspot balancing user initiative and system support in the development of an effective and user-centric critiquing-based recommender syste

Immersive Learning Environments for Computer Science Education

This master\u27s thesis explores the effectiveness of an educational intervention using an interactive notebook to support and supplement instruction in a foundational-level programming course. A quantitative, quasi-experimental group comparison method was employed, where students were placed into either a control or a treatment group. Data was collected from assignment and final grades, as well as self-reported time spent using the notebook. Independent t-tests and correlation were used for data analysis. Results were inconclusive but did indicate that the intervention had a possible effect. Further studies may explore better efficacy, implementation, and satisfaction of interactive notebooks across a larger population and multiple class topics