Cognitive design elements to support the construction of a mental model during software exploration

2012 [AS13b, Wal12a]. 2013 [Alv13, Bar13, Cam13, Car13, CCS13, Cut13, Edi14, Swe13]. 2014 [CDS13, DN14, ND14, Rod14, Tym14a]. 2015 [DeL14a, DeL14b, She15b]. 2016 [AT16a, CCV16, CCV17, MR15, SDCT16a, SDCT16c, She16a]. 2017 [DG17, EC17, Fit18b, MJ18, TCSM17, TCM17]. 2018 [BG18b, DD17, Dec19b, Gol18, JI18, MKMP18, MMK18, PQH17, She18]. 2019 [Cut19, FI19, HPQ19, MP19a, MPRM19, MP19b, MS19, Rod18, She19a]. 2020 [DH19].

/pdf/a-complete-bibliography-of-publications-in-sigcse-bulletin-1aolnsl5m4.pdf

A Complete Bibliography of Publications in SIGCSE Bulletin: 2000{2009

One of the main challenges for teachers in facilitating and orchestrating collaborative activities within multiple groups is that they cannot see information in real time and typically see only the final product of the groups' activity. This is a problem as it means that teachers may find it hard to be aware of the learners' collaborative processes, partial solutions and the contribution of each student. Emerging shared devices have the potential to provide new forms of support for face-to-face collaboration and also open new opportunities for capturing and analysing the collaborative process. This can enable teachers to monitor students' learning more effectively. This paper presents an interactive dashboard that summarises student data captured from a multi-tabletop learning environment and allows teachers to drill down to more specific information when required. It consists of a set of visual real-time indicators of the groups' activity and collaboration. This study evaluates how teachers used the dashboard determine when to intervene in a group. The key contributions of the paper are the implementation and evaluation of the dashboard, which shows a form of learner model from a concept mapping tabletop application designed to both support collaborative learning and capture traces of activity.

An interactive teacher's dashboard for monitoring groups in a multi-tabletop learning environment

Classroom teaching becomes viable and efficient based on increase in participation of the student. This can be made possible by taking needed measure by finding the emotions of the students. Many researchers worked on emotion identification of students. Now-a-days sentiment analysis using deep learning models have gained good performance. Especially ensemble Long Short-Term Memory (LSTM) with attention layers gives more attention to the influence word on the emotion. In the proposed method, input sequences of sentences are processed parallel across multi-head attention layer with fine grained embeddings (Glove and Cove) and tested with different dropout rates to increase the accuracy. Later in this paper, the information from both deep multi-layers is fused and fed as input to the LSTM layer. In this paper, we conclude that the fusion of multiple layers accompanied with LSTM improves the result over a common Natural Language Processing method.

Sentiment analysis of student feedback using multi-head attention fusion model of word and context embedding for LSTM

Educational data mining (EDM) is an important research area that is used to improve education by monitoring students performance and trying to understand the students’ learning. Taking feedback from students at the end of the semester, however, has the disadvantage of not beneﬁtting the students that have already taken the course. To beneﬁt the current students, feedback should be given in real time and addressed in real time. This would enable students and lecturers to address teaching and learning issues in the most beneﬁcial way for the students. Analysing students’ feedback using sentiment analysis techniques can identify the students’ positive or negative feelings, or even more reﬁned emotions, that students have towards the current teaching. Feedback can be collected in a variety of ways, with previous research using student response systems such as clickers, SMS and mobile phones. This paper will discuss how feedback can be collected via social media such as Twitter and how using sentiment analysis on educational data can help improve teaching. The paper also introduces our proposed system Sentiment Analysis for Education (SA-E).

/pdf/sa-e-sentiment-analysis-for-education-2gbcz5v7ga.pdf

SA-E: Sentiment Analysis for Education

The development of multi-touch tables, an emerging technology for classroom learning, offers valuable opportunities to explore how its features can be designed to support effective collaboration in schools. In this study, small groups of 10- to 11-year-old children undertook a history task where they had to connect various pieces of information about a mining accident to reach a consensus about who had been responsible. Their interaction using traditional resources was compared with their interaction when using a multi-touch table. Analysis suggests that the design and capabilities of the multi-touch technology offers some key features that supported the collaboration and interaction of the participants, particularly in the early stages of the task. Some of these features appear to provide new opportunities for collaboration and interaction, which were different from the interactions observed in the paper-based groups. These features of the multi-touch surface therefore appear to support effective interaction between the pupils.

Practitioner Notes
What is already known about this topic

• Research suggests that collaborative interaction supports learning.
• Features such as the nature of the task and the social interaction influence these outcomes.


What this paper adds

• This paper looks specifically at the possibilities when a large multi-touch table is used to support collaboration.
• It compares paper-based and multi-touch versions of the same activity involving school pupils.


Implications for practice and/or policy

• Multi-touch surfaces can support collaborative interaction.
• The initial stages of the task were significantly different.
• Pupils should be encouraged to reach a consensus about what they have to do and how they are going to do it as well as encouraged to produce a joint solution.

/pdf/multi-touch-tables-and-collaborative-learning-13nmb4395d.pdf

Multi-Touch Tables and Collaborative Learning.

The potential of tabletops to enable simultaneous interaction and face-to-face collaboration can provide novel learning opportunities. Despite significant research in the area of collaborative learning around tabletops, little attention has been paid to the integration of multi-touch surfaces into classroom layouts and how to employ this technology to facilitate teacher-learner dialogue and teacher-led activities across multi-touch surfaces. While most existing techniques focus on the collaboration between learners, this work aims to gain a better understanding of practical challenges that need to be considered when integrating multi-touch surfaces into classrooms. It presents a multi-touch interaction technique, called TablePortal, which enables teachers to manage and monitor collaborative learning on students' tables. Early observations of using the proposed technique within a novel classroom consisting of networked multi-touch surfaces are discussed. The aim was to explore the extent to which our design choices facilitate teacher-learner dialogue and assist the management of classroom activity.

Towards a teacher-centric approach for multi-touch surfaces in classrooms

This paper demonstrates how sentiment analysis can be used to identify differences in how students and staff perceive the opinions contained in feedback for programming work. The feedback considered in this paper is conceptually different in that it is given in the form of tags that when associated with a fragment of source code can be considered as a sharable learning resource. The research presented investigates the differences in perception of whether feedback is positive, negative or neutral according to students and examiners. This paper also investigates the adequacy of an automated sentiment analysis engine with a view that sentiment information when combined with the feedback tag and source code may create a more informative sharable learning resource. This paper describes the investigatory technique and presents the initial results. Results indicate that there are important differences between the sentiment of feedback perceived by students and examiners. This paper highlights the benefit of including sentiment data along with feedback.

Using Feedback Tags and Sentiment Analysis to Generate Sharable Learning Resources Investigating Automated Sentiment Analysis of Feedback Tags in a Programming Course

Encouraging collaborative software design through the use of multi-touch interfaces has become increasingly important, because such surfaces can accommodate more than one user concurrently, which is particularly useful for collaborative software design. In this paper, the potential of using multi-touch MT-CollabUML application for collaborative software design is explored. This exploration is done by looking at how students' collaboration might be enhanced in collaborative software design using Unified Modeling Language (UML) comparing the traditional paper-based environment with the contemporary multi-touch table environment.

Exploring the significance of multi-touch tables in enhancing collaborative software design using UML

Multi-touch interfaces facilitate collaborative learning and, thus, represent a promising educational technology. Their ability to synchronously accommodate multiple users is an advantage in co-located collaborative design tasks. This paper explores the multi-touch interface's potential in collaborative Unified Modelling Language (UML) diagramming by comparing it to a PC-based tool. The results of the study demonstrate that the use of the multi-touch table enables an increase in the equity of participation, enhanced collaboration amongst team members, and the facilitation of parallel-participative design.

/pdf/a-multi-touch-interface-for-enhancing-collaborative-uml-50kp604iy3.pdf

A multi-touch interface for enhancing collaborative UML diagramming

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