Constructive Feedback Course

This short online course aims to help the students develop an understanding of what constructive feedback is and offers them an opportunity to practise giving such feedback in the context of texts related to science, engineering and technology. The course is conceived as a preparation course for the EAST Project during which engineering students from Gaza were asked to act as critical friends offering content-oriented feedback to pre-sessional students at the University of Glasgow. We believe the course can be offered on its own, either in isolation or as part of a wider mentoring scheme for students who want to develop skills in ‘leadership by example’[2]. The course can easily be adapted for any discipline - this would require changing the samples in tasks 5 and 6. The course is intended to be delivered over two weeks and it is run online and asynchronously. In terms of design, it follows the framework of exploration - integration - application (Garrison and Arbaugh, 2007), with each stage being progressively more complex, challenging and open-ended. The students work in groups and the teacher monitors interactions and motivates them to stay on task and track in regard with deadlines via a closed Facebook group. The final task requires submitting an assessed assignment which the tutor gives feedback on. After that there is an extra week for reflection and evaluation. Timings for each activity are approximate and may need to be adjusted depending on the general progress of the course. See the course overview for details. The main technology used is Google Docs and it is recommended that the students have gmail accounts. Although students without gmail accounts are still able to access and edit the materials, tracking their contributions will not be possible. When setting up the documents, attention has to be paid to shareability settings so that there is no barrier to access. Padlet is used for sharing personal experiences and reflections at the beginning and end of the course respectively. A closed Facebook group is used as a news and discussions forum and for collective feedback. A public blog may be used for documenting the process. All the documents are created by the teacher; he or she creates an empty template for each group[3] and pastes the subsequent tasks into them on the task start date. This increases the teacher’s workload but helps them monitor their students’ progress and has a motivational effect on the students. An alternative approach would to be to ask the students to copy the template, and the task instructions could be provided in other ways, for example via Facebook, in order to decrease the workload. The course can be enriched by synchronous sessions during which the teacher can provide collective feedback and the students have an opportunity to ask questions and express their concerns. Adding a video element, for example to introduce the whole course, individual tasks or to give feedback, may constitute an added value too

Supporting Constructive Learning with a Feedback Planner

A promising approach to constructing more effective computer tutors is implementing tutorial strategies that extend over multiple turns. This means that computer tutors must deal with (1) failure, (2) interruptions, (3) the need to revise their tactics, and (4) basic dialogue phenomena such as acknowledgment. To deal with these issues, we need to combine ITS technology with advances from robotics and computational linguistics. We can use reactive planning techniques from robotics to allow us to modify tutorial plans, adapting them to student input. Computational linguistics will give us guidance in handling communication management as well as building a reusable architecture for tutorial dialogue systems. A modular and reusable architecture is critical given the difficulty in constructing tutorial dialogue systems and the many domains to which we would like to apply them. In this paper, we propose such an architecture and discuss how a reactive planner in the context of this architecture can implement multi-turn tutorial strategies

Constructive Feedback Course

This short online course aims to help the students develop an understanding of what constructive feedback is and offers them an opportunity to practise giving such feedback in the context of texts related to science, engineering and technology. The course is conceived as a preparation course for the EAST Project during which engineering students from Gaza were asked to act as critical friends offering content-oriented feedback to pre-sessional students at the University of Glasgow. We believe the course can be offered on its own, either in isolation or as part of a wider mentoring scheme for students who want to develop skills in ‘leadership by example’[2]. The course can easily be adapted for any discipline - this would require changing the samples in tasks 5 and 6. The course is intended to be delivered over two weeks and it is run online and asynchronously. In terms of design, it follows the framework of exploration - integration - application (Garrison and Arbaugh, 2007), with each stage being progressively more complex, challenging and open-ended. The students work in groups and the teacher monitors interactions and motivates them to stay on task and track in regard with deadlines via a closed Facebook group. The final task requires submitting an assessed assignment which the tutor gives feedback on. After that there is an extra week for reflection and evaluation. Timings for each activity are approximate and may need to be adjusted depending on the general progress of the course. See the course overview for details. The main technology used is Google Docs and it is recommended that the students have gmail accounts. Although students without gmail accounts are still able to access and edit the materials, tracking their contributions will not be possible. When setting up the documents, attention has to be paid to shareability settings so that there is no barrier to access. Padlet is used for sharing personal experiences and reflections at the beginning and end of the course respectively. A closed Facebook group is used as a news and discussions forum and for collective feedback. A public blog may be used for documenting the process. All the documents are created by the teacher; he or she creates an empty template for each group[3] and pastes the subsequent tasks into them on the task start date. This increases the teacher’s workload but helps them monitor their students’ progress and has a motivational effect on the students. An alternative approach would to be to ask the students to copy the template, and the task instructions could be provided in other ways, for example via Facebook, in order to decrease the workload. The course can be enriched by synchronous sessions during which the teacher can provide collective feedback and the students have an opportunity to ask questions and express their concerns. Adding a video element, for example to introduce the whole course, individual tasks or to give feedback, may constitute an added value too

Constructive Preference Elicitation over Hybrid Combinatorial Spaces

Preference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized "from scratch" by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In the constructive setting, most existing elicitation techniques fail because they rely on exhaustive enumeration of the candidates. A previous solution explicitly designed for constructive tasks comes with no formal performance guarantees, and can be very expensive in (or unapplicable to) problems with non-Boolean attributes. We propose the Choice Perceptron, a Perceptron-like algorithm for learning user preferences from set-wise choice feedback over constructive domains and hybrid Boolean-numeric feature spaces. We provide a theoretical analysis on the attained regret that holds for a large class of query selection strategies, and devise a heuristic strategy that aims at optimizing the regret in practice. Finally, we demonstrate its effectiveness by empirical evaluation against existing competitors on constructive scenarios of increasing complexity.Comment: AAAI 2018, computing methodologies, machine learning, learning paradigms, supervised learning, structured output

Addressing the learners' needs for specific and constructive feedback

This paper discusses an on-going project which proposes to make feedback to students more personal, explicit and more useful as a method of further engaging students. It addresses an issue that has recently been identified by the researchers where students on an Engineering programme were not recognising the presence of feedback on their assessed work. Feedback is central to the process of learning. However it has been widely accepted, through tools such as the UK National Student Survey, that students are still relatively dissatisfied with the feedback they have received. There is therefore a need to ensure that feedback given to students is specific and constructive in terms of helping them move their own learning forward. A pilot is being carried out with two Engineering classes, offering students the option to request specific feedback on their class tests. The students are asked to identify the areas of their work they require feedback on through the completion of a ‘feedback request label’. Staff can then respond to the feedback requests and issue students with personal and relevant feedback. Initial findings from the project have shown students to have clear expectations regarding the type of feedback they want. Students identified that they expect clear and legible feedback, which draws together feedback comments from throughout their work and provides a summary of how they could move forwards. Findings have also identified some differences in expectations and perceptions of feedback between the Level 3 and Level 4 students involved in the project

Key Findings and Recommendations from Medina Foundation 2015 Grantee Perception Report

In May and June 2015, The Center for Effective Philanthropy (CEP) conducted a survey of the Medina's grantees. The memo below outlines the key findings from Medina's GPR as well as the methodology used to collect this feedback. CEP has included comments below that reference both positive and negative feedback from grantees. The proportion of negative or constructive comments in this narrative is over-represented relative the full set of grantee comments

Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.Comment: Accepted at the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18