The goal of this study is to better understand the cognitive processing of confirmatory (positive) feedback and critical (negative) feedback to improve the delivery of feedback. To do this, researchers collected learning analytics via a behavioral assessment game and an eye tracker to investigate attention allocation to different feedback valences (positive and negative feedback). This study evaluates if there is a difference in the participants' attendance between confirmatory feedback and critical feedback.


Eye Tracking the Feedback Assigned to Undergraduate Students in a Digital Assessment Game-
Goal of the Study

The goal of this study is to work toward developing more effective feedback-targeting systems for tutoring software, to improve user performance. In this study, researchers built a tutoring software system that uses Natural Language Processing (NLP) to provide adaptive feedback to students in an online learning task. 
- The researcher's system models the interaction between a student's natural language response and the software, which interprets students' responses and provides the best feedback action. This framework allows for the optimization of a reward signal. 
- Researchers showed that natural-language based targeting policies were able to choose optimal feedback from fewer exercises compared to multiple-choice based policies. The natural-language policies were effective even when tested on novel interactions. 

Adaptive Natural-Language Targeting for Student Feedback- Goal of Study

- Feedback in education
- Achievement gap in education 
- Self regulation in education
- Student demographics
- Academic performance
- Data architecture

Focuses of Learning Analytics

The goal of this study is to quantitatively analyze how STEM instructors at the college level use their time on the first day of classes and to qualitatively analyze how their messages can be characterized in order to understand the learning environment being established within the course.

Making a First Impression: Exploring What Instructors Do and Say on the First Day of Introductory STEM Courses - Goal of Study

Learning analytics (LA) is the idea that datasets acquired through teaching or learning activities can be used to enhance student learning, academic progress, and teaching practices.

University of Georgia

San Jose State University

- Cognitive theory of drawing construction
- Learning by conceptual level
- Expectancy-valence theory
- Self-determination Theory (SDT)
- Modes of learning
- Efficacy of Traditional Teaching Methods
- Constructivism
- Engagement
- Learning Analytics (LA)
- Community of Inquiry Framework (CoI)
- Achievement Gap in Education
- Desirable difficulties

Focuses of Learning Science

Humans can only learn one or at most two conceptual levels at a time. A person is able to learn the next level of concepts only after they have a stable understanding of the original. From then on, we are able to fine tune our understanding of the lower level concepts, while our learning focus is on the next level of abstraction.

Learning by Conceptual Levels

Readers may translate text into visual information by selecting, organizing and then integrating. Graphical representations such as concept maps, flow diagrams, and tree diagrams can be used to depict the structural and conceptual relationships found in text. By generating diagrams, readers are able to leverage spatial memory to develop a coherent understanding of the text.

Cognitive Theory of Drawing Construction

People behave based on the value that they perceive doing the behavior has to them. It takes into account expectancy (effort), instrumentality (performance) and valence (value/reward).

Expectancy-Valence Theories

Analyzes the effectiveness of passive teaching styles (i.e. lecturing or exams) on student learning. 

Efficacy of Traditional Teaching Methods

A theory of human motivation that posits that the reasons why an individual chooses to participate and exerts effort, are based on self-determined regulations (such as intrinsic motivation). It states there are 3 desires that promote optimal function and growth:
- 1) Autonomy
- 2) Competence
- 3) Relatedness

Self-determination theory (SDT)

The measure and study of sustained interaction with the medium. Engagement is different from Motivation in that it focuses on the user's willingness to continue interacting, rather than what inspires or spurs them into action.

Engagement

Learning Analytics

- MOOCs (massive open online courses)
- Blended Learning
- Online Learning
- Distance Learning
- E-learning

Mode of Education

The intersecting three-presence framework, consisting of Social, Teaching and Cognitive Presences, that represented all the important activities in an educational experience.  

Community of Inquiry Framework

A proposed hierarchy of understanding that can be used to evaluate a student's comprehension of a topic. From the highest level to the lowest level of understanding, they are:
- Creation / Create
- Evaluation / Evaluate
- Analysis / Analyze
- Application / Apply
- Comprehension / Understand
- Knowledge / Remember


Bloom's Taxonomy of Learning

A learning task that requires a considerable but desirable amount of effort, thereby improving long-term performance.

Examples:

- Interleaving Materials
- Spacing Studying
- Generation Effect
- Retrieval practice (testing)
- Varying Study Conditions

Desirable Difficulties

A theory that recognizes the learners' understanding and knowledge based on their own experiences prior to entering school. Rather than simply taking in knowledge, learners take the opportunity to reinforce and build their own understanding of the topic, derived from what they already know and what is being presented to them.

Constructivism (in Education)

The achievement gap is a persistent disparity in academic outcomes, such as grades, test scores, and graduation rates, observed among students from different demographic groups. These groups are often defined by ethnicity, race, and, in certain subjects, gender.

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Related

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