6,696 research outputs found
Design and evaluation of teacher assistance tools for exploratory learning environments
We present our approach to designing and evaluating tools that can assist teachers in classroom settings where students are using Exploratory Learning Environments (ELEs), using as our case study the MiGen system, which targets 11-14 year old students' learning of algebra. We discuss the challenging role of teachers in exploratory learning settings and motivate the need for visualisation and notification tools that can assist teachers in focusing their attention across the whole class and inform their interventions. We present the design and evaluation approach followed during the development of MiGen's Teacher Assistance tools, drawing parallels with the recently proposed LATUX workflow but also discussing how we go beyond this to include a large number of teacher participants in our evaluation activities, so as to gain the benefit of different view points. We discuss the results of the evaluations, which show that participants appreciated the capabilities of the tools and were mostly able to use them quickly and accurately
Graph-based modelling of students' interaction data from exploratory learning environments
Students' interaction data from learning environments has
an inherent temporal dimension, with successive events
being related through the ``next event'' relationship.
Exploratory learning environments (ELEs), in particular, can
generate very large volumes of such data,
making their interpretation a challenging task.
Using two mathematical microworlds as exemplars,
we illustrate how modelling students' event-based interaction data as a graph can open up new querying and analysis opportunities.
We demonstrate the possibilities that graph-based modelling
can provide for querying and analysing the data,
enabling investigation of student-system interactions
and leading to the improvement of future versions of
the ELEs under investigation
Similarity-based grouping to support teachers on collaborative activities in exploratory learning environments
This paper describes a computer-based tool that helps teachers group their students for collaborative activities in the classroom, the challenge being to organise groups of students based on their recent work so that their collaboration results in meaningful interactions. Students first work on an exploratory task individually, and then the computer suggests possible groupings of students to the teacher. The complexity of the tasks is such that teachers would require too long a time to create meaningful groups. The paper describes the design of the tool, the algorithms and metrics used for generating the groups, the evaluation of the tool, and the pedagogical context in which the tool was designed
Visualisation and analysis of students’ interaction data in exploratory learning environments
Log files from adaptive Exploratory Learning Environments
can contain prohibitively large quantities of data
for visualisation and analysis.
Moreover, it is hard to know in advance what data is required for analytical purposes.
Using a microworld for secondary algebra as a case study,
we discuss how students' interaction data
can be transformed into a data warehouse
in order to allow its visualisation and exploration using
online analytical processing (OLAP) tools.
We also present some additional, more targeted,
visualisations of the students' interaction data.
We demonstrate the possibilities that these visualisations provide for exploratory data analysis, enabling confirmation or contradiction of expectations that pedagogical experts may have about the system and
ultimately providing both empirical evidence and insights
for its further development
Utilization of a tool to help intensivists in the implementation and monitoring of the ventilator-associated pneumonia bundle protocol running in an adult medical – surgical critical care unit
The impact of feedback on students’ affective states
Affective states play a significant role in students' learning behaviour. Positive affective states can enhance learning, while negative affective states can inhibit it. This paper describes a Wizard-of-Oz study that investigates the impact of different types of feedback on students' affective states. Our results indicate the importance of providing feedback matched carefully to the affective state of the students in order to help them transition into more positive states. For example when students were confused affect boosts and specific instructive feedback seem to be effective in helping students to be in flow again. We discuss this and other ways to adapt the feedback, together with implications for the development of our system and the field in general
Affect matters: exploring the impact of feedback during mathematical tasks in an exploratory environment
We describe a Wizard-of-Oz study that investigates the impact of different types of feedback on students’ affective states. Our results indicate the importance of matching carefully the affective state with appropriate feedback in order to help students transition into more positive states. For example when students were confused affect boosts and specific instruction seem to be effective in helping students to be in flow again. We discuss this and other effective ways to and implications for the development of our system and the field in general
Using graph-based modelling to explore changes in students’ affective states during exploratory learning tasks
This paper describes how graph-based modelling can be used to explore interactions associated with a change in students' affective state when they are working with an exploratory learning environment (ELE). We report on a user study with an ELE that is able to detect students' affective states from their interactions and speech. The data collected during the user study was modelled, visualized and queried as a graph. We were interested in exploring if there was a difference between low- and high-performing students in the kinds of interactions that occurred during a change in their affective state. Our findings provide new insights into how students are interacting with the ELE and the effects of the system's interventions on students' affective states
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