 Reader and Task The Forgotten Component of Text Complexity Sydnee Dickson, Utah State Office of Education Jimi Cannon, Scholastic Classroom and Community Group

Anticipation Chart True or False, Why? Statement True or False, Why? What the READER brings to the reading of a text is more important than the Lexile © level of a text. The only way we can support students in processing complex text is by creating TASKs that are not rigorous.

Objectives Participants will:  Review three prongs of text complexity.  Choose complex texts that support readers.  Create appropriate tasks for complex texts.  Explore ways to support teachers in addressing “reader and task.”

 Text Complexity Using the excerpts from the Common Core, Appendix A, draw a graphic representation of one of the three components of text complexity.

Quantitative: Choosing Text  CLASS LEXILE RANGE: 720L-1020L  GROUP: 720L-750L  INSTRUCTIONAL FOCUS: Identifying Main Idea

680L 730L 740L 780L 730L 750L

Qualitative: Choosing Text  CLASS RANGE: N-S  GROUP: N/O  INSTRUCTIONAL FOCUS: Character Development

GR O GR N GR L GR K GR N GR Q

Reader and Task Reader Motivation Knowledge Task Type of Reading Intended Outcome

 Choosing Text Group A: Qualitative & Reader Group B: Quantitative & Reader

A + B =

Reader and Task Reader Motivation Knowledge Task Type of Reading Intended Outcome

Task: Type of Reading  READING  Teacher reads aloud the text the first time. The students re-read the text for a different purpose a second time.  Partners read the text together.  Teacher reads aloud the text.  Students read the text independently.

Task: Type of Reading  You decided to do an excerpt from this book with the whole class.  How might you have the class read this text since they have a range of reading levels? (680L-830L) (GR Level P – GR Level U) 800L GR Level T

Task: Intended Outcome

Based on the group of students identified on your task card:  Identify the type of reading that will best support the students.  Create a task that will be appropriate for your intended outcome. 740L/GR Level R

School Level Support  Professional learning opportunities to help teachers address reader and task:  Collaborative lesson design and study  Peer observation with feedback  Developing common formative assessments  Topic of discussion in learning communities

District Level Support  Face to face practice with colleagues  Instructional coaching with feedback  Formative assessments varied by DOK  Teacher developed quality lessons/units  Video exemplars of quality instruction

State Level Support  Technology based Lexile © identification  Bank of exemplar tasks matched to reader  Online collaboratives/communities  Rubrics and tools for determining DOK, Lexiles ©, appropriate tasks, etc.

Anticipation Chart True or False, Why? Statement True or False, Why? What the READER brings to reading influences his or her ability to understand complex text is more important than the Lexile © level of a text. The only way we can support students in processing complex text is by creating TASKs that are not rigorous.

 Reader and Task The Forgotten Component of Text Complexity Sydnee Dickson, Utah State Office of Education Jimi Cannon, Scholastic Classroom and Community Group

Building Knowledge 1. Read the Title. 2. Read the first paragraph. 3. Read the last paragraph. 4. Tell a partner everything thing you learned from what you have read so far. 5. In your own words, jot down what you learned. Unlocking Complex Text, Laura Robb. 2015

A Review of Audio Fingerprinting

Audio fingerprinting is best known for its ability to link unlabeled audio to corresponding meta- data (e.g. artist and song name), regardless of the audio format. Audio fingerprinting or content- based audio identification (CBID) systems extract a perceptual digest of a piece of audio content, i.e. a fingerprint and store it in a database. When presented with unlabeled audio, its fingerprint is calculated and matched against those stored in the database. Using fingerprints and matching algorithms, distorted versions of a recording can be identified as the same audio content.

We have presented a review of the research carried out in the area of audio fingerprinting. Furthermore a number of applications which can benefit from audio fingerprinting technology were discussed. An audio fingerprinting system generally consists of two components: an algorithm to generate fingerprints from recordings and algorithm to search for a matching fingerprint in a fingerprint database. We have shown that although different researchers have taken different approaches, the proposals more or less fit in a general framework. In this framework, the fingerprint extraction includes a front-end where the audio is divided into frames and a number of discriminative and robust features is extracted from each frame. Subsequently these features are transformed to a fingerprint by a fingerprint modeling unit which further compacts the fingerprint representation.