Teaching for Fluency with Teaching for Fluency with Information Technology:Information Technology:The Role of Feedback in The Role of Feedback in Instructional Design and Instructional Design and Student AssessmentStudent Assessment

Explorations in Instructional Technology

Mark Urban-Lurain

Don Weinshank

October 27, 2000

www.cse.msu.edu/~cse101

OverviewOverview

Context: Teaching non-CS majors Instructional Design Uses of technology Results Implications

Fluency with Information Fluency with Information TechnologyTechnology

What does it mean to be a literate college graduate in an information age?

Information technology is ubiquitous “Computer Literacy” associated with training Being Fluent with Information Technology (FIT)

Committee on Information Technology Literacy, 1999

CSE 101 MSU introductory course for non-CS majors

Instructional Principles Instructional Principles 1. Concepts and principles promote transfer within domain

Necessary for solving new problems

2. “Assessment drives instruction” (Yelon) “Write the final exam first”

3. Move focus from what is taught to what is learned Student-centered

4. Formative evaluation improves student performance Study – test – restudy – retest

5. Performance assessments evaluate mastery of concepts High inter-rater reliability critical

6. Mastery-model learning ensures objectives met What students can do, not what they can say

Uses of Technology in Uses of Technology in InstructionInstruction

Delivery of content Television CBI / CBT Web-based

Communication E-mail Discussion groups Real-time chat

Feedback and monitoring Formative evaluation Iterative course development and improvement

`Incoming Students

Instruction

Assessment

Outcomes

Design Inputs

Instructional Goals

Instructional Design

Design Phase

Implementation Phase

Course Design & ImplementationCourse Design & Implementation

Discriminant AnalysisDiscriminant Analysis

Multivariate statistical classification procedure Dependent variable: final course grade Independent variables

Incoming student data Classroom / instructional data Assessment performance data

Each student classified in group with highest probability Evaluate classification accuracy

Interpret discriminant functions Independent variable correlations with functions Similar to interpreting loadings in Factor Analysis

3

6

7

5

4

2

FootnoteModify styleWeb formatPrivate folder

New SSChartsFunctions

Path

Find application

TOC

URL

Public folder

Update SS

Boolean search

Skill to Schema MapSkill to Schema Map

LinkWeb search

Find rename file

Computer specs.

SIRS: Course, TA, ATASIRS: Course, TA, ATA

End of semester student survey about course Three Factors

“Fairness” Student preparation and participation Course resources

SIRS for Lead TA One Factor

SIRS for Assistant TA One Factor

Fairness FactorFairness Factor

35.3% of variance on this factor accounted for by: Final grade in course TA SIRS Number of BT attempts ATA SIRS Cumulative GPA ACT Mathematics Computer specifications Incoming computer communication experience

Participation FactorParticipation Factor

19.8% of variance on this factor accounted for by: TA SIRS Attendance ATA SIRS ACT Social Science Number of BT attempts Create chart Incoming knowledge of computer terms ACT Mathematics Find - rename file Path TOC

NO course grade

Course Resources FactorCourse Resources Factor

11.3 % of variance on this factor accounted for by: TA SIRS Attendance ATA SIRS Extension task: backgrounds Web pages in Web folder Number of BT attempts

NO course grade

Lead TA SIRSLead TA SIRS

27.8 % of variance on Lead TA SIRS accounted for by: Fairness factor Preparation and participation factor TA Experience Course resources factor ATA SIRS Attendance Private folder Extension task: excel function Number of BT attempts

NO course grade

Assistant TA SIRSAssistant TA SIRS

13.4 % of variance on ATA SIRS accounted for by: Fairness factor Preparation and participation factor Student E-mail factor TA SIRS Course resources factor Attendance Path TA Experience

NO course grade

Technology in Instructional Technology in Instructional Design and Student AssessmentDesign and Student Assessment

Data-rich instructional system Detailed information about each student CQI for all aspects of instructional system

Performance-based assessments Labor intensive Inter-rater reliability Analyzing student conceptual frameworks

Intervention strategies Early identification Targeted for schematic structures

ImplicationsImplications

Instructional design process can be used in any discipline Accreditation Board for Engineering and Technology CQI

Distance Education Demonstrates instruction at needed scale On-line assessments How to provide active, constructivist learning on line?

Questions?Questions?

CSE 101 Web site

www.cse.msu.edu/~cse101

Instructional design detail slidesInstructional design detail slides

Design Inputs• Literature

• CS0• Learning• Assessment

• Client department needs• Design team experience

Design InputsDesign Inputs

Instructional Goals• FITness• Problem solving• Transfer• Retention• No programming

Instructional GoalsInstructional Goals

Deductive InstructionDeductive Instruction

Concept

Skill 1 Skill 2

Skill 3

Schema 1 Schema 2

Schema 3

Inductive InstructionInductive Instruction

Skill 1

Skill 3

Concept

Skill 2

Instructional Design• 1950 students / semester• Multiple “tracks”

• Common first half• Diverge for focal problems

• All lab-based classes• 65 sections• No lectures

• Problem-based, collaborative learning• Performance-based assessments

Instructional DesignInstructional Design

Incoming Students• Undergraduates in non-technical majors

• GPA• ACT scores• Class standing• Major• Gender• Ethnicity• Computing experience

Incoming StudentsIncoming Students

Instruction• Classroom staff

• Lead Teaching Assistant• Assistant Teaching Assistant

• Lesson plans• Problem-based learning

• Series of exercises• Homework• Instructional resources

• Web, Textbook

InstructionInstruction

Assessment• Performance-based• Modified mastery model• Bridge Tasks

• Determine grade through 3.0• Formative• Summative

• Final project• May increase 3.0 to 3.5 or 4.0

AssessmentAssessment

Bridge Task Competencies Bridge Task Competencies in CSE 101in CSE 101

1.0 E-mail; Web; Distributed network file systems; Help 1.5 Bibliographic databases; Creating Web pages 2.0 Advanced Word-processing 2.5 Spreadsheets (functions, charts); Hardware; Software 3.0Track A

Advanced Web site creation; Java Applets; Object embedding

3.0 Track C Advanced spreadsheets; Importing; Data analysis; Add-on tools

3.0 Track D Advanced spreadsheets; Fiscal analysis; Add-on tools

Bridge Task Detail Drilldown 1Bridge Task Detail Drilldown 1

BridgeTask (BT)Database

• Each Bridge Task (BT) has dimensions (M) that define

the skills and concepts being evaluated.

• Within each dimension are some number of instances

(n) of text describing tasks for that dimension.

• A bridge task consists of one randomly selected

instance from each dimension for that bridge task

Dim 1Instance i

Instance i+1Instance i+2Instance i+n

Dim 2Instance i

Instance i+1Instance i+2Instance i+n

Dim MInstance i

Instance i+1Instance i+2Instance i+n

Creating AssessmentsCreating Assessments

BridgeTask (BT)Database

Dim 1Instance i

Instance i+1Instance i+2Instance i+n

Dim 2Instance i

Instance i+1Instance i+2Instance i+n

Dim MInstance i

Instance i+1Instance i+2Instance i+n

Dim 1Instance 1

Criteria iCriteria i+1Criteria i+2Criteria i+n

Dim 2Instance i+2

Criteria iCriteria i+1Criteria i+2Criteria i+n

Dim MInstance i+n

Criteria iCriteria i+1Criteria i+2Criteria i+n

Student EvaluationPASS or FAIL

Evaluation CriteriaEvaluation Criteria

Bridge Task Detail Drilldown 2Bridge Task Detail Drilldown 2

Web Server

Student Enters: Pilot ID

PIDPW

StudentRecordsDatabase

Submits to Create Query

BridgeTask (BT)Database

RequestNew BT

Dim 1Instance i

Instance i+1Instance i+2Instance i+n

Dim 2Instance i

Instance i+1Instance i+2Instance i+n

Dim MInstance i

Instance i+1Instance i+2Instance i+n

Randomly select one instance from each of M dimensions for desired BT

Assemble Text

Dim 1 (i+1)Dim 2 (i+n)Dim M (i)

Web Server

IndividualStudent

BT Web PageReturns

Delivering AssessmentsDelivering Assessments

StudentRecordsDatabase

Grader Queuing Create Query

BridgeTask (BT)Database

RequestCriteria

Dim 1Criteria i

Criteria i+1Criteria i+2Criteria i+n

Dim 2Criteria i

Criteria i+1Criteria i+2Criteria i+n

Dim MCriteria i

Criteria i+1Criteria i+2Criteria i+n

Provide criteria for instances used toconstruct student’s BT

IndividualStudent

BT ChecklistReturns

Grader evaluateseach criteria

PASS or FAIL

StudentBridge

Task

Dim

1

Cri

teri

a

Dim

2

Cri

teri

a

Dim

M C

rite

ria

StudentRecordsDatabase

Record PASS / FAIL for each criteria

Evaluating AssessmentsEvaluating Assessments

OutcomesOutcomes

Outcomes• Student final grades• SIRS• TA feedback• Oversight Committee: Associate Deans• Client department feedback