Public feedback - but personal feedforward?

Academic feedback is taken here as the reporting to student writers of the strengths and weaknesses of their submitted draft work, while academic feedforward refers to constructive advice regarding possible strengthening of students’ next work. Both originate from a tutor’s initial judgement of a student’s work. Feedback and feedforward on work showing need for improvement are problematic in a Confucian Heritage Culture. Even gently constructive advice within a programme seeking evidence for assessment of critical thinking may lead to perception of hurtful criticism by Taiwanese students. Some could withdraw from class activity accordingly. So the writers adjusted their response style. They now choose between different approaches featuring tutorial feedback or feedforward, depending on the standard of work being judged. When individual postings feature poor critical thinking, the writers opt for private messages concentrating on constructive feedforward. For better postings, they provide positive feedback with reasons for their judgements, and summarise to the class these exemplars of generic strengths in critical thinking. They also offer private prompting when they see scope for further enrichment of an able student’s critical thinking. This might also be a useful practice when tutoring solely in the West

The learner’s role in assessing higher level abilities

This paper responds to the vision of a new assessment culture, which will be a meaningful integration of teaching, learning and assessment. It reviews the advice in the literature about assessment, and from there identifies principles for the assessment of the development of higher level abilities in various domains. It envisages the student as an active participant in the development of criteria and standards, and their consequent use in the making of judgements. The suggested principles are tested out on a recent conventional experience that brought the writers together as tutor and student, and on their subsequently proposed generic model

An original framework for understanding human actions and body language by using deep neural networks

The evolution of both fields of Computer Vision (CV) and Artificial Neural Networks (ANNs) has allowed the development of efficient automatic systems for the analysis of people's behaviour. By studying hand movements it is possible to recognize gestures, often used by people to communicate information in a non-verbal way. These gestures can also be used to control or interact with devices without physically touching them. In particular, sign language and semaphoric hand gestures are the two foremost areas of interest due to their importance in Human-Human Communication (HHC) and Human-Computer Interaction (HCI), respectively. While the processing of body movements play a key role in the action recognition and affective computing fields. The former is essential to understand how people act in an environment, while the latter tries to interpret people's emotions based on their poses and movements; both are essential tasks in many computer vision applications, including event recognition, and video surveillance. In this Ph.D. thesis, an original framework for understanding Actions and body language is presented. The framework is composed of three main modules: in the first one, a Long Short Term Memory Recurrent Neural Networks (LSTM-RNNs) based method for the Recognition of Sign Language and Semaphoric Hand Gestures is proposed; the second module presents a solution based on 2D skeleton and two-branch stacked LSTM-RNNs for action recognition in video sequences; finally, in the last module, a solution for basic non-acted emotion recognition by using 3D skeleton and Deep Neural Networks (DNNs) is provided. The performances of RNN-LSTMs are explored in depth, due to their ability to model the long term contextual information of temporal sequences, making them suitable for analysing body movements. All the modules were tested by using challenging datasets, well known in the state of the art, showing remarkable results compared to the current literature methods

Feedforward practices: a systematic review of the literature

The notion of ‘feedforward’ has emerged as popular with practitioners, and there has been an upsurge in publications which include this term. This interpretivist and conceptual systematic review sought to consider the different forms of educational practices that are framed in relation to feedforward. The initial search of four electronic databases found 1076 articles published between 2007 and 2019, which were reduced to 68 once duplicates had been removed and exclusion/inclusion criteria applied during screening and eligibility procedures. An iterative meta-ethnographic approach to analysis resulted in the identification of five main practices, framed as feedforward. These were: alignment and timing (41%); use (25%); comments (18%); self-review (9%); and teaching (7%). The vast majority involved a process where student improvement was a key goal, but the design of this process differed between practices. A large proportion supported improvement from one task to the next, almost exclusively within the ‘future horizon’ of the module/study unit, while only a small proportion of articles focuses on improving the amount, nature or quality of the information delivered to learners. Evidence of student sense-making and uptake was rarely sought, and few practices offered genuine opportunities for student agency, self-regulation and the development of evaluative judgment

Enriching Affect Analysis Through Emotion and Sarcasm Detection

Affect detection from text is the task of detecting affective states such as sentiment, mood and emotions from natural language text including news comments, product reviews, discussion posts, tweets and so on. Broadly speaking, affect detection includes the related tasks of sentiment analysis, emotion detection and sarcasm detection, amongst others. In this dissertation, we seek to enrich textual affect analysis from two perspectives: emotion and sarcasm. Emotion detection entails classifying the text into fine-grained categories of emotions such as happiness, sadness, surprise, and so on, whereas sarcasm detection seeks to identify the presence or absence of sarcasm in text. The task of emotion detection is particularly challenging due to limited number of resources and as it involves a greater number of categories of emotions in which to undertake classification, with no fixed number or types of emotions. Similarly, the recently proposed task of sarcasm detection is complicated due to the inherent sophisticated nature of sarcasm, where one typically says or writes the opposite of what they mean. This dissertation consists of five contributions. First, we address word-emotion association, a fundamental building block of most, if not all, emotion detection systems. Current approaches to emotion detection rely on a handful of manually annotated resources such as lexicons and datasets for deriving word-emotion association. Instead, we propose novel models for augmenting word-emotion association to support unsupervised learning which does not require labeled training data and can be extended to flexible taxonomies of emotions. Second, we study the problem of affective word representations, where affectively similar words are projected into neighboring regions of an n-dimensional embedding space. While existing techniques usually consider the lexical semantics and syntax of co-occurring words, thus rating emotionally dissimilar words occurring in similar contexts as highly similar, we integrate a rich spectrum of emotions into representation learning in order to cluster emotionally similar words closer, and emotionally dissimilar words farther from each other. The generated emotion-enriched word representations are found to be better at capturing relevant features useful for sentence-level emotion classification and emotion similarity tasks. Third, we investigate the problem of computational sarcasm detection. Generally, sarcasm detection is treated as a linguistic and lexical phenomena with limited emphasis on the emotional aspects of sarcasm. In order to address this gap, we propose novel models of enriching sarcasm detection by incorporating affective knowledge. In particular, document-level features obtained from affective word representations are utilized in designing classification systems. Through extensive evaluation on six datasets from three diverse domains of text, we demonstrate the potential of exploiting automatically induced features without the need for considerable manual feature engineering. Motivated by the importance of affective knowledge in detecting sarcasm, the fourth contribution of this thesis seeks to dig deeper and study the role of transitions and relationships between different emotions in order to discover which emotions serve as more informative and discriminative features for distinguishing sarcastic utterances in text. Lastly, we show the usefulness of our proposed affective models by applying them in a non-affective framework of predicting the helpfulness of online reviews

Assessment and feedback in higher education: considerable room for improvement?

No abstract available

Assessment and feedback in higher education: considerable room for improvement?

No abstract available