- Lea's Blog

From an article by Virginia "Jenny" Williams

Formative assessment is a teaching tool that supports all learners, but it is especially critical for students who are struggling as it holds the potential for changing the learning outcome. When teachers use formative assessment during learning tasks, students get immediate feedback on their learning. This is an important consideration for students that struggle because corrective feedback is a necessity during the learning process. As a struggling learner, students often do not realize why they are struggling. Providing them with immediate feedback draws attention to the tasks that support their learning and those that don’t. Awareness of what works for them as a learner, and what doesn’t, allows them to begin to recognize the supports they need during the learning process and when they need to use them.

Students identify strategies that help them learn.
As students identify the strategies that help them learn, they begin to recognize that they can use this information to support the learning process. Knowing how to learn, or in this case, the way that one learns best, separates the learner, the content, and the strategy they are using into different categories.  When students can differentiate themselves from content and learning strategies, they begin to take control of their learning.

Students take control of their learning.
Having control over the learning process further promotes a positive growth mindset, which in turn, promotes success at learning. In this scenario, a positive cycle for learning is established and successes are highlighted, instead of failures. This type of success promotes effort rather than intelligence as a primary element of the learning process.

Smaller “chunks” of content help students process more effectively.
Finally, lessons that include multiple formative assessment events and strategies break learning tasks into manageable parts. This helps students focus their learning, as time demands are reduced and feedback is provided multiple times at shorter intervals. Utilizing shorter intervals for focusing helps students that have difficulty with maintaining attention to task. This also breaks content into manageable “chunks” that students can stop and process, before moving to the next component of the learning task.

Universities departing standardized tests, use rubric to assess students
Illustration by Olivia Falcigno/Daily Free Press

An assessment method tested by 59 colleges and universities could be used in K-12 schools as well

Extracts from an article by Katrina Schwartz

Right now, some universities require a small sample of their students to take a standardized test before graduating, but many administrators and faculty find this method problematic. Students have no personal investment in the test, and it is divorced from the coursework that they see as their primary objective while in college. Also, not everyone does well on tests, but they may shine in their coursework.

Concerns over the effectiveness of standardized tests prompted the Association of American Colleges and Universities to begin working on a rubric-based alternative that is consistent and valid.

First, they set out to define the essential learning outcomes that faculty, employers and accreditors saw as important. They settled on 16 qualities, some of which are: critical thinking, writing, quantitative literacy, oral communication, ethics, teamwork, intercultural understanding, and integrating learning from one area to another.

For the first-year pilot study they focused only on three of those outcomes: written communication, critical thinking and quantitative literacy. The faculty worked together to write rubrics (called Valid Assessment of Learning in Undergraduate Education or VALUE rubrics) that laid out what a progression of these skills looks like. The rubrics were tested on campuses and rewritten three times before reaching a final version.

In a pilot study of the rubrics, 127 trained scorers evaluated 7,000 samples of student work across a variety of disciplines. Because they were grading the cross-cutting skills of written communication, critical thinking and quantitative literacy, faculty evaluated work from disciplines that were not their own.

“These rubrics are designed to be cross-disciplinary,” explained Bonnie Orcutt, associate professor of economics at Worcester State University and temporarily the director of Learning Outcomes Assessment for the Massachusetts Department of Higher Education.“ I can look at something and have no idea if the content was correct, but that’s not what I’m looking for. Independent of whether the content is correct, they may have used a body of evidence really well, have good organization, good syntax, good citations.”

In other words the facts might be all wrong, but the person is a good writer, which is what the scorer is trying to evaluate with this rubric.

Bridging the gap to K-12

So far, this rubric work has been happening only at two- and four-year universities. But the conversation happening in higher education isn’t so different from that going on in K-12 schools. Parents and teachers are pushing back against blunt assessment instruments like standardized tests, and are looking for a way to hold schools accountable that doesn’t mean taking time away from class work.

Many K-12 educators and parents would like to see a similar type of system in their schools. Many welcome assessment and see the need to make sure kids are learning, but they’d like to see those evaluations happening based on the work students produce for class in context that they care about.

Image source: Rukuku, Inc.

From an article by Marcelo Tibau

Educational data mining

The need for understanding how students learn is the major force behind educational data mining. The suite of computational and psychological methods and research approaches supported by interactive learning methods and tools, such as intelligent tutoring systems, simulations, games, have opened up opportunities to collect and analyze student data and to discover patterns and trends in those data. Data mining algorithms help find variables that can be explored for modeling and by applying data mining methods that classify data and find relationships, these models can be used to change what students experience next or even to recommend outside academic assignments to support their learning.

An important feature of educational data is that they are hierarchical. All the data (from the answers, the sessions, the teachers, the classrooms, etc.) are nested inside one another. Grouping it by time, sequence, and context provide levels of information that can show the impact of the practice sessions length or the time spent to learning – as well as how concepts build on one another and how practice and tutoring should be ordered. Providing the right context to these information help to explain results and to know where the proposed instructional strategy works or not. The methods that have been important to stimulate developments in mining educational data are those related to:

  1. Prediction, for understanding what behaviors in an online learning environment, such as participation in discussion forums and taking practice tests, can be used to predict outcome such as which students might fail a class. It also helps to forecast or understand student educational outcomes, such as success on posttests after tutoring.
  2. Clustering, i.e. finding data points that naturally group together and can be used to split a full dataset into categories. Examples of clustering are grouping students based on their learning difficulties and interaction patterns, or grouping by similarity of recommending actions and resources.
  3. Relationship, i.e. discovering relationships between variables in a dataset and encoding them as rules for later use. These techniques can be used to associate student activity (in a learning management system or discussion forums) with student grades, to associate content with user types to build recommendations for content that is likely to be interesting or even to make changes to teaching approaches. This latter area, called teaching analytics, is of growing importance and key to discover which pedagogical strategies lead to more effective or robust learning.
  4. Distillation, which is a technique that involves depicting data in a way that enables humans to quickly identify or classify features of the data. This area of educational data mining improves machine learning models by allowing humans to easier identify patterns or features, such as student learning actions, student behaviors or collaboration among students.
  5. Model discovery, which is a technique that involves using a validated model (developed through such methods as prediction or clustering) as a component in further analysis. Discovery with models supports discovery of relationships between student behaviors and student characteristics or contextual variables, analysis of research questions across a wide variety of contexts, and integration of psychometric modeling into machine learned models.

Learning Analytics

Learning analytics emphasizes measurement and data collection as activities necessary to undertake, understand, analyze and report data for educational purposes. Unlike educational data mining, learning analytics generally does not emphasize reducing learning into components but instead seeks to understand entire systems and to support human decision making. It draws on a broad array of academic disciplines, incorporating concepts from information science, computer science, sociology, statistics, psychology, and learning sciences.

The goal is to answer important questions that affect student learning and organizational learning systems. Therefore, it emphasizes models that could answer questions such as:

    When are students ready to move on to the next topic?
    When is a student at risk for not completing a course?
    What is the best next course for a given student?
    What kind of help should be provided?

As a visual representation of analytics is critical to generate actionable analyses, the information is often represented as “dashboards” that show data in an easily digestible form.

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Excerpted from an article by Kathy Dyer

The old adage that less is more is certainly at the heart of self assessment and self-regulation, one of the cornerstones of successful formative assessment. While it goes without saying that teachers teach, getting out of the way and giving students the means and opportunity to self assess and self-regulate their thinking, learning, and work – not only individually, but also with other students – can have a huge impact on meeting their learning targets.

Teachers need to empower their students and give them a leading role in their own education. It’s no doubt that most students are their own biggest critics, and that’s okay; focusing that lens can have fantastic results. By engaging in the process of thinking about and assessing their own work, they act on the evidence of their own learning and take responsibility for it.

Research on the self-regulation of learning, including self assessment and self-monitoring, indicates that students who engage in these activities are more likely to develop internal attributions, a feeling of empowerment, and a sense of autonomy. One study in particular by Fernandes and Fontana in 1996 (Changes in the control beliefs in Portuguese primary school pupils as a consequence of the employment of self-assessment strategies. British Journal of Educational Psychology, 66, 301–313) established a training program of self-assessment strategies with 25 primary school teachers. Over a period of eight months, the teachers implemented these strategies within their classrooms. Students in these classrooms were compared to students in the classrooms of 20 control teachers. Results indicated that students who are provided with regular opportunities and encouragement to engage in self-assessment are more likely to attribute their learning to internal beliefs; that is, students believe they can have an impact on their own learning. These students were less likely to attribute success to luck or other unknown variables and were more likely to identify the real causes of academic success.

In 2004 Sue Brookhart and some of her colleagues examined the impact of student self-monitoring on 41 students in two classrooms (Minute Math: An action research study of student self-assessment. Educational Studies in Mathematics, 57, 213–227). Students were provided with structures and tools (logs, graphs, reflection sheets, etc.) to reflect each week on the success of their studying and problem-solving strategies. An analysis of student reflection sheets showed that when teachers involved their students in monitoring their own progress, students were more autonomous and were able to accurately predict their performance on timed tests. Overall, the students in this study enjoyed participating in self-assessment and liked seeing their progress.

Jan Synek

Rainstorms and Symphonies

Source WolfTrapMedia
(from Musical Rain—Sequencing a Music Experience with Instruments and Props)

When early elementary teachers integrate music and theater, student learning improves in reading, math, and science as they become better critical thinkers and problem solvers.

An article by Mary Gresock and Lisette Steinwald describes a rainstorm experience in which preschool and kindergarten teachers and Wolf Trap Institute-trained teaching artists use singing, dancing, and other elements of the performing arts to engage young children in more active classroom experiences and inspire learning through all of their senses. When students are exposed to arts-integrated teaching, Wolf Trap has found that learning improves across subject areas, including reading, math, and science, as they become better critical thinkers and problem solvers.

The beauty of arts integration is how the knowledge and creativity turn into a cycle of their own -- not only do the students internalize the content, but the content also serves as the vehicle for them to become artists.


The value of the performing arts in early childhood education

Independent studies of the Wolf Trap Institute model, research from the arts education and early childhood fields, and Wolf Trap’s more than 30 years of experience affirm that the infusion of arts-integration strategies into curriculum content enhances early childhood development.

This includes language and literacy, social/emotional growth, STEM skills (science, technology, engineering, and math), and “21st Century skills”—critical thinking, problem solving, communication, collaboration, and creativity.

A 2006 independent study showed that preschool children who participated in a Wolf Trap Institute’s arts-integrated residency program, Fairfax Pages, scored significantly higher on standardized tests measuring 6 key areas: initiative, social relations, creative representation, language and literacy, logic and mathematics, and movement and music. Children were assessed before and after implementation of Wolf Trap’s program using the standardized, nationally validated Child Observation Record (COR), an observational assessment tool designed by High/Scope Educational Research Foundation and implemented in Fairfax County Public Schools and early childhood programs administered by the Fairfax County Office for Children. (Klayman, 2006) The Fairfax Pages study was supported through major funding from the U.S. Department of Education.

The graph above illustrates the comparison of students in the Pages program and those who did not articipate.

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Lea's Learning Analytics Blog

Learning analytics, educational data mining, formative assessment - all recent buzz words in educational research. In principle, the idea is to find theoretical frameworks, models, procedures, and smart tools to collect, aggregate, analyze, reason on and visualize large scale educational data. LEA’s BOX is a research and development project funded by the European Commission. The project aims at (a) making educational assessment and appraisal more goal-oriented, proactive, and beneficial for students, and (b) at enabling formative support of teachers and other educational stakeholders on a solid basis of a wide range of information about learners. That means, LEA’s BOX is a learning analytics toolbox that is intended to enable educators to perform competence-centered, multi-source learning analytics. More info at http://www.leas-box.eu!


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