15,314 research outputs found
Effects of Automated Interventions in Programming Assignments: Evidence from a Field Experiment
A typical problem in MOOCs is the missing opportunity for course conductors
to individually support students in overcoming their problems and
misconceptions. This paper presents the results of automatically intervening on
struggling students during programming exercises and offering peer feedback and
tailored bonus exercises. To improve learning success, we do not want to
abolish instructionally desired trial and error but reduce extensive struggle
and demotivation. Therefore, we developed adaptive automatic just-in-time
interventions to encourage students to ask for help if they require
considerably more than average working time to solve an exercise. Additionally,
we offered students bonus exercises tailored for their individual weaknesses.
The approach was evaluated within a live course with over 5,000 active students
via a survey and metrics gathered alongside. Results show that we can increase
the call outs for help by up to 66% and lower the dwelling time until issuing
action. Learnings from the experiments can further be used to pinpoint course
material to be improved and tailor content to be audience specific.Comment: 10 page
Reply With: Proactive Recommendation of Email Attachments
Email responses often contain items-such as a file or a hyperlink to an
external document-that are attached to or included inline in the body of the
message. Analysis of an enterprise email corpus reveals that 35% of the time
when users include these items as part of their response, the attachable item
is already present in their inbox or sent folder. A modern email client can
proactively retrieve relevant attachable items from the user's past emails
based on the context of the current conversation, and recommend them for
inclusion, to reduce the time and effort involved in composing the response. In
this paper, we propose a weakly supervised learning framework for recommending
attachable items to the user. As email search systems are commonly available,
we constrain the recommendation task to formulating effective search queries
from the context of the conversations. The query is submitted to an existing IR
system to retrieve relevant items for attachment. We also present a novel
strategy for generating labels from an email corpus---without the need for
manual annotations---that can be used to train and evaluate the query
formulation model. In addition, we describe a deep convolutional neural network
that demonstrates satisfactory performance on this query formulation task when
evaluated on the publicly available Avocado dataset and a proprietary dataset
of internal emails obtained through an employee participation program.Comment: CIKM2017. Proceedings of the 26th ACM International Conference on
Information and Knowledge Management. 201
User Review-Based Change File Localization for Mobile Applications
In the current mobile app development, novel and emerging DevOps practices
(e.g., Continuous Delivery, Integration, and user feedback analysis) and tools
are becoming more widespread. For instance, the integration of user feedback
(provided in the form of user reviews) in the software release cycle represents
a valuable asset for the maintenance and evolution of mobile apps. To fully
make use of these assets, it is highly desirable for developers to establish
semantic links between the user reviews and the software artefacts to be
changed (e.g., source code and documentation), and thus to localize the
potential files to change for addressing the user feedback. In this paper, we
propose RISING (Review Integration via claSsification, clusterIng, and
linkiNG), an automated approach to support the continuous integration of user
feedback via classification, clustering, and linking of user reviews. RISING
leverages domain-specific constraint information and semi-supervised learning
to group user reviews into multiple fine-grained clusters concerning similar
users' requests. Then, by combining the textual information from both commit
messages and source code, it automatically localizes potential change files to
accommodate the users' requests. Our empirical studies demonstrate that the
proposed approach outperforms the state-of-the-art baseline work in terms of
clustering and localization accuracy, and thus produces more reliable results.Comment: 15 pages, 3 figures, 8 table
Web API Fragility: How Robust is Your Web API Client
Web APIs provide a systematic and extensible approach for
application-to-application interaction. A large number of mobile applications
makes use of web APIs to integrate services into apps. Each Web API's evolution
pace is determined by their respective developer and mobile application
developers are forced to accompany the API providers in their software
evolution tasks. In this paper we investigate whether mobile application
developers understand and how they deal with the added distress of web APIs
evolving. In particular, we studied how robust 48 high profile mobile
applications are when dealing with mutated web API responses. Additionally, we
interviewed three mobile application developers to better understand their
choices and trade-offs regarding web API integration.Comment: Technical repor
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