Creativity: Generating Diverse Questions using Variational Autoencoders

Generating diverse questions for given images is an important task for computational education, entertainment and AI assistants. Different from many conventional prediction techniques is the need for algorithms to generate a diverse set of plausible questions, which we refer to as "creativity". In this paper we propose a creative algorithm for visual question generation which combines the advantages of variational autoencoders with long short-term memory networks. We demonstrate that our framework is able to generate a large set of varying questions given a single input image.Comment: Accepted to CVPR 201

Using an extended food metaphor to explain concepts about pedagogy

It is anathema for educators to describe pedagogy as having a recipe - it is tantamount to saying it is a technicist process rather than a professional one requiring active, informed decision-making. But if we are to help novice teachers understand what pedagogy is and how it can be understood, there must be a starting point for pedagogical knowledge to shape both the understanding and design of appropriate curriculum learning. In order to address this challenge, I argue that food preparation processes and learning how to competently cook are analogous to understanding how pedagogy - also about process, design, and making knowledge knowable - facilitates learning about teaching specific curriculum knowledge. To do so, I use evidence from an ITE cohort lecture on pedagogy as a case study. In essence, viewing pedagogy through the lens of food and recipes may help make some abstractions of pedagogy more concrete and make some principles of pedagogy more accessible to novice teachers as they learn to design learning

ToMChallenges: A Principle-Guided Dataset and Diverse Evaluation Tasks for Exploring Theory of Mind

Theory of Mind (ToM), the capacity to comprehend the mental states of distinct individuals, is essential for numerous practical applications. With the development of large language models, there is a heated debate about whether they are able to perform ToM tasks. Previous studies have used different tasks and prompts to test the ToM on large language models and the results are inconsistent: some studies asserted these models are capable of exhibiting ToM, while others suggest the opposite. In this study, We present ToMChallenges, a dataset for comprehensively evaluating Theory of Mind based on Sally-Anne and Smarties tests. We created 30 variations of each test (e.g., changing the person's name, location, and items). For each variation, we test the model's understanding of different aspects: reality, belief, 1st order belief, and 2nd order belief. We adapt our data for various tasks by creating unique prompts tailored for each task category: Fill-in-the-Blank, Multiple Choice, True/False, Chain-of-Thought True/False, Question Answering, and Text Completion. If the model has a robust ToM, it should be able to achieve good performance for different prompts across different tests. We evaluated two GPT-3.5 models, text-davinci-003 and gpt-3.5-turbo-0301, with our datasets. Our results indicate that consistent performance in ToM tasks remains a challenge.Comment: work in progres

HANS, are you clever? Clever Hans Effect Analysis of Neural Systems

Instruction-tuned Large Language Models (It-LLMs) have been exhibiting outstanding abilities to reason around cognitive states, intentions, and reactions of all people involved, letting humans guide and comprehend day-to-day social interactions effectively. In fact, several multiple-choice questions (MCQ) benchmarks have been proposed to construct solid assessments of the models' abilities. However, earlier works are demonstrating the presence of inherent "order bias" in It-LLMs, posing challenges to the appropriate evaluation. In this paper, we investigate It-LLMs' resilience abilities towards a series of probing tests using four MCQ benchmarks. Introducing adversarial examples, we show a significant performance gap, mainly when varying the order of the choices, which reveals a selection bias and brings into discussion reasoning abilities. Following a correlation between first positions and model choices due to positional bias, we hypothesized the presence of structural heuristics in the decision-making process of the It-LLMs, strengthened by including significant examples in few-shot scenarios. Finally, by using the Chain-of-Thought (CoT) technique, we elicit the model to reason and mitigate the bias by obtaining more robust models

Improving subject knowledge and subject pedagogic knowledge in employment based secondary initial teacher training in England

Each year in England about 6,000 trainee teachers qualify by undertaking an employment-based initial teacher training route (EBITT), where training is mainly school based. Government inspectors have found that trainees on this route are weaker in subject knowledge and subject pedagogic knowledge compared to trainees following the more traditional one year training course (PGCE) of which about a third of course time is University based. EBITT providers are currently seeking to improve the subject knowledge aspect of training. To support this work the TDA have published a model for developing trainees' subject knowledge for teaching and suggest that providers review their provision against the model. In addition EBITT providers must also meet a new requirement that the total training time should be a minimum of 60 days. This new requirement presents a challenge to EBITT providers as most of the subject knowledge enhancement will have to be school-based. This paper seeks to find out: - how trainee teachers acquire subject and subject pedagogic knowledge while based in a school and - whether teaching staff in schools have the required subject and subject pedagogic knowledge and skills for this enhanced role. Data have been collected from trainees, school-based mentors, school-based Initial Teacher Training Coordinators and University assessors over a one year period. Data about the way trainees acquire subject knowledge was interpreted against the TDA model. The study finds that: - trainees acquire subject and subject pedagogic knowledge in a variety of highly individualistic ways that suggests that the TDA model only partially explains what is happening in practice and - there is a significant training need to ensure schools are well equipped to deliver high quality subject focussed training.</p

The Psychology of Epistemic Judgment

Human social intelligence includes a remarkable power to evaluate what people know and believe, and to assess the quality of well- or ill-formed beliefs. Epistemic evaluations emerge in a great variety of contexts, from moments of deliberate private reflection on tough theoretical questions, to casual social observations about what other people know and think. We seem to be able to draw systematic lines between knowledge and mere belief, to distinguish justified and unjustified beliefs, and to recognize some beliefs as delusional or irrational. This article outlines the main types of epistemic evaluations, and examines how our capacities to perform these evaluations develop, how they function at maturity, and how they are deployed in the vital task of sorting out when to believe what others say

IS THERE A LEARNING TYPE?! REVISITING LEARNING-STYLES THEORY IN VIEW OF LEARNING AND EMOTION

Over the last twenty years the VAK learning-styles theory, which differentiates between visual, auditory, and kinesthetic learning types, has been criticized and debunked by various academic disciplines and declared by several scientists as a neuromyth. Regardless of its criticism, the concept has retained its popularity within the teacher-community and is regularly taught in teacher education. The aim of this article is to meet this theory-practice gap in a constructive way. After (1) a short introduction, this paper starts (2) with a differentiated assessment of the theory. The VAK learning-style theory will be deconstructed into four main hypotheses which are then (3) one at a time, evaluated (empirically as well as in view of teaching practice). After a complex evaluation of the concept and its criticism, this article continues with (4) showing, how the learning-style theory provides teachers with an approachable understanding of learning and comforts them in dealing with learning differences within a heterogenic student body. Considering the empirical evidence on the one hand and teacher’s needs on the other hand, this article (5) lines out fundamental insights of learning theories, as well as (6) the relevance and capacity of emotions for perception and evaluation processes. Approaching (7) learning style-theory from the perspective of learning-theories and theories of emotion, which highlights the interdependency of learning, achievement, and emotion, finally allows concluding the paper (8) with four specific and normative principles, which allows teachers to benefit from an empirical accurate understanding of a complex process of learning and teaching.  Article visualizations