DOT ENUMERATION, SYMBOLIC NUMBER COMPARISON, AND MENTAL NUMBER LINE ESTIMATION SKILLS IN DETERMINING STUDENTS’ DYSCALCULIA TENDENCIES* SINAN OLKUN, ZEYNEP.

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Presentation transcript:

DOT ENUMERATION, SYMBOLIC NUMBER COMPARISON, AND MENTAL NUMBER LINE ESTIMATION SKILLS IN DETERMINING STUDENTS’ DYSCALCULIA TENDENCIES* SINAN OLKUN, ZEYNEP AKKURT DENIZLI Faculty of Education, TED University, Turkey Faculty of Educational Sciences, Ankara Univ, Turkey 10th international conference DisCo 2015: From analog education to digital education, organized by Faculty of Arts of Charles University. On 22.-24.6.2015, Prag, Czech Republic * Support for this work was provided by the Scientific and Technological Research Council of Turkey (TÜBİTAK) under the grant number 111K545.

Significance Why Math is difficult for many students but not so much for others How the students with MLD are different from the other students who can function normally in math classes? Is there a reason that we discover and measure somehow? Research shows that there are some unique tasks on which students have different capabilities This is what we call number sense Performance in these tasks correlate with curriculum based math achievement as well as calculation performance

2) Symbolic Number Comparison, SNC The Aim To investigate whether it was possible to determine students with MLD risk with a screening tool containing 3 basic number processing tests (BNPT). Dot counting Symbolic number comparison Mental numberline estimations 1) Canonic Dot Counting, CDC 2) Symbolic Number Comparison, SNC 3) Mental Number Line, MNL

METHODS Participants: Five tests were administered to the students. 478 students from 12 state primary schools in Ankara Five tests were administered to the students. Firstly students were administered a curriculum based Mathematics Achievement Test (MAT) Then, they were divided into three groups MLD risk, low achieving, typical achieving based on this achievement test. The three other tests, Canonic Dot Counting (CDC) Test, Symbolic Number Comparison (SNC) Test, and Mental Number Line (MNL) Test were administered to students individually. These tests were administered on TABLET PCs and used to measure the basic number processing skills of the students. The last test, RAVEN

A Typical CDC Task A Canonic Dot Counting task on TABLET PC 21 tasks

A TYPICAL SNC TASK A Symbolic Number Comparison task on TABLET PC 36 tasks, 12 consistent, 12 inconsistent, 12 neutral

A TYPICAL MNL TASK A Mental Number Line task on TABLET PC Three types of numberlines: 0-10; 0-100; 0-1000 33 tasks for 1st and 2nd graders, 58 tasks for 3rd and 4th graders

ANALYSIS Inverse Efficiency Scores (IES) were calculated for two of the BNP tests, CDC, and SNC Total Absolute Errors (TAE) were calculated for MNL estimations The scores obtained from the BNP tests are expected to be inversly proportional to MAT score

RESULTS 11 of the 12 first graders, categorized as MLD risk, got scores lower than grade level mean, in at least one BNPT A first grader scored above 50% in RAVEN but below 25% in MAT, and above 65% in SNC and above 85% in CDC

RESULTS all of the 2nd, 3rd, and 4th graders, categorized as MLD risk, got scores lower than grade level mean, in at least one BNPT A Second grader scored above 50% in RAVEN but below 35% in MAT, and above 65% in MNL and CDC

RESULTS A 4th grader scored above 50% in RAVEN but below 35% in MAT, and above 60% in MNL, above 65% in SNC, above 55% in CDC A 4th grader scored above 90% in RAVEN but below 30% in MAT, and above 60% in MNL and SNC, above 95% in CDC

CONCLUSIONS The results showed that the basic number processing tests (BNPT) currently developed in tablet PC environment was very effective in determining MLD risk in primary school students, especially at the second, third and fourth grade, third grade being the most effective. This means that screening tool has a very high potential to determine students with MLD risk at the primary school level Once the students’ defficiencies in basic numerical skills are determined it would be possible to arrange intervention studies based on individual needs. MLDR students may not have difficulty in all of the tasks. In other words, students might be good at in one test but not so good at in other tests. Therefore, in such a screening tool it is better to include different types of tasks in order to chatch as many types of difficulty as possible.