Messages exchanged between participants in online discussion forums often contain personal names and other details that need to be redacted before the data is used for research purposes in learning analytics. However, removing the names entirely makes it harder to track the exchange of ideas between individuals within a message thread and across threads, and thereby reduces the value of this type of conversational data. In contrast, the consistent use of pseudonyms allows contributions from individuals to be tracked across messages, while also hiding the real identities of the contributors. Several factors can make it difficult to identify all instances of personal names that refer to the same individual, including spelling errors and the use of shortened forms. We developed a semi-automated approach for replacing personal names with consistent pseudonyms. We evaluated our approach on a data set of over 1,700 messages exchanged during a distance-learning course, and compared it to a general-purpose pseudonymisation tool that used deep neural networks to identify names to be redacted. We found that our tailored approach out-performed the general-purpose tool in both precision and recall, correctly identifying all but 31 substitutions out of 2,888.

We present the EuroPat corpus of patent-specific parallel data for 6 official European languages paired with English: German, Spanish, French, Croatian, Norwegian, and Polish. The filtered parallel corpora range in size from 51 million sentences (Spanish-English) to 154k sentences (Croatian-English), with the unfiltered (raw) corpora being up to 2 times larger. Access to clean, high quality, parallel data in technical domains such as science, engineering, and medicine is needed for training neural machine translation systems for tasks like online dispute resolution and eProcurement. Our evaluation found that the addition of EuroPat data to a generic baseline improved the performance of machine translation systems on in-domain test data in German, Spanish, French, and Polish; and in translating patent data from Croatian to English. The corpus has been released under Creative Commons Zero, and is expected to be widely useful for training high-quality machine translation systems, and particularly for those targeting technical documents such as patents and contracts.

Asynchronous online discussions within a community of learners can improve learning outcomes through social knowledge construction, but the depth and quality of student contributions often varies widely. Approaches to assessing critical discourse typically use content analysis to identify indicators that correspond to framework constructs, that in turn serve as measures of depth and quality. Often only a single construct is addressed for performing content analysis in the literature, although recent work has used both social presence and cognitive presence constructs from the Community of Inquiry (CoI) framework. Nevertheless, there is no effective, commonly used, analytic approach to combining insights from multiple perspectives about quality and depth of online discussions. This paper addresses the gap by proposing the combined use of cognitive engagement (the ICAP framework) and cognitive presence (CoI); and by proposing a network analytic approach that quantifies the associations between the two frameworks and measures the moderation effects of two instructional interventions on those associations. The present study found that these associations were moderated by one intervention but not the other; and that messages labelled with the most common phase of cognitive presence could be usefully assigned to smaller meaningful subgroups by also considering the mode of cognitive engagement.

This paper describes work in progress to answer the question of how we can identify and model the depth and quality of student participation in class discussion forums using the content of the discussion forum messages. We look at two widely-studied frameworks for assessing critical discourse and cognitive engagement: the ICAP and Community of Inquiry (CoI) frameworks. Our goal is to discover where they agree and where they offer complementary perspectives on learning.

The widespread use of online discussion forums in educational settings provides a rich source of data for researchers interested in how collaboration and interaction can foster effective learning. Such online behaviour can be understood through the Community of Inquiry framework, and the cognitive presence construct in particular can be used to characterise the depth of a student's critical engagement with course material. Automated methods have been developed to support this task, but many studies used small data sets, and there have been few replication studies.

Many social media platforms support the curation of personal digital data, and, more recently, the use of that data for review and reflection. We explored the process of reflection by asking users to create a meaningful ‘triptych’ of photographs drawn from their Facebook accounts. In a first study, we asked participants to manually trawl their own accounts and select three relevant images, which we then framed and used as an interview probe. In a second study, we designed an automated triptych generation system and assessed participants’ experiences of using this system. We conducted qualitative analyses of participant interviews from both studies. Consistent with other ‘slow technology’ work, we found the act of creating a physical artefact from social media data gave that data new meaning, albeit with notable differences between manual versus automatically generated triptychs. We conclude by discussing possible improvements to the design of the automated triptych system.

We present the Beetle-Grow intelligent tutoring system, which combines active experimentation, self-explanation, and formative feedback using natural language interaction. It runs in a standard web browser and has a fresh, engaging design. The underlying back-end system has previously been shown to be highly effective in teaching basic electricity and electronics concepts. Beetle-Grow has been designed to capture student interaction and indicators of learning in a form suitable for data mining, and to support future work on building tools for interactive tutoring that improve after experiencing interaction with students, as human tutors do.

We will demonstrate the Beetle-Grow intelligent tutoring system, which combines active experimentation, self-explanation, and formative feedback using natural language interaction. It runs in a standard web browser and has a fresh, engaging design. The underlying back-end system has previously been shown to be highly effective in teaching basic electricity and electronics concepts.

We propose that a set of five dimensions forms a foundation underlying a number of prevalent theoretical perspectives on learning. We show how student contributions to instructional dialogue can be reliably annotated with these dimensions. Finally, we provide preliminary validation evidence for our coding scheme and illustrate the potential value of such an approach to analyzing student behavior in tutorial dialogue.

Our research goal is to investigate whether previous findings and methods in the area of tutorial dialogue can be generalized across dialogue corpora that differ in domain (mechanics versus electricity in physics), modality (spoken versus typed), and tutor type (computer versus human). We first present methods for unifying our prior coding and analysis methods. We then show that many of our prior findings regarding student dialogue behaviors and learning not only generalize across corpora, but that our methodology yields additional new findings. Finally, we show that natural language processing can be used to automate some of these analyses.