Rediscovery of Initial Habituation Importance Learned from Analytics of Learning Check Testing in Mathematics for Undergraduate Students

Hirose, Hideo; Takatou, Masanori; Yamauchi, Yusuke; Kubo, Fujio; Taniguchi, Tetsuji; Imaoka, Mitsunori; Koyama, Tetsuya

doi:10.1109/iiai-aai.2017.43

Cited by 12 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To evaluate the LCT results numerically, we have adopted the IRT scheme in the risk analysis in [8], [10] as seen in the appendix. In LCT, the number of questions in one testing is so small such as five or seven because of the limited testing time.…”

Section: Success/failure Responses and The Lct Ability Valuesmentioning

confidence: 99%

“…A brief introduction to this online testing system applied to more than 1,000 freshman students is illustrated in the appendix. The system has been successfully operating (see [5], [6]), and some computational results were reported [8], [10], where, importance of initial habituation for learning using LCT are shown. In addition, other relevant cases have been investigated (see [7], [9], [11], [13], [15]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Key Factor Not to Drop Out is to Attend Lectures

Hirose

2019

Information Engineering Express

Self Cite

View full text Add to dashboard Cite

To find key factors not to drop out using learning analytics, we have added accumulated data such as the number of successes in learning check testing, the number of attendances to follow-up program classes, and etc., in addition to learning check testing ability scores performed at each lecture. Then, we have found key factors strongly related to the students at risk. They are the following. 1) Badly failed students (score range is 0-39 in the term ex-amination) tend to be absent for the regular classes and fail in learning check testing even if they attended, and they are very reluctant to attend follow-up program classes. 2) Success-ful students (score range is 60-100 in the term examination) attend classes and obtain good scores in every learning check testing. 3) Failed students but not so badly (score range is 40-59 in the term examination) reveal both sides of features appeared in score range of 0-39 and score range of 60-100. Therefore, it is crucial to attend lectures in order not to drop out. Students who failed in learning check testing more than half out of all testing times almost absolutely failed in the term examination, which could cause the drop out. Also, students who were successful to learning check testing more than two third out of all testing times took better score in the term examination.

show abstract

Section: Success/failure Responses and The Lct Ability Valuesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Key Factor Not to Drop Out is to Attend Lectures

Hirose

2019

Information Engineering Express

Self Cite

View full text Add to dashboard Cite

show abstract

“…where δ i, j denotes the indicator function such that δ = 1 for success and δ = 0 for failure in answering a question. We adopt the IRT evaluation for students' abilities unlike the case in [19].…”

Section: Ability Evaluation Using the Irtmentioning

confidence: 99%

“…In such systems, we included learning check testing (LCT) for every class to measure student comprehension of lectures. The system has been successfully operating (see [16], [17]), and some computational results have been reported [19]. In addition, other relevant aspects have been investigated (see [18], [20], [21], [22], [26], [30]).…”

Section: Introductionmentioning

confidence: 99%

“…This paper is aimed at developing effective learning strategies of students at risk of failing courses and/or dropping out, using the large-scale learning data collected from online testings. In this paper, unlike conventional methods using correct answer rate (CAR) to identify proficiency of a student (e.g., see [19]), we use the ability obtained from item response theory (IRT, e.g., see [1], [7], [23]), and we introduce a new method to identify students at risk as early as possible using the IRT results.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Success or Failure for Examination using Nearest Neighbor Method to the Trend of Weekly Online Testing

Hirose

2019

International Journal of Learning Technologies and Learning Environments

Self Cite

View full text Add to dashboard Cite

Using trends of estimated abilities in terms of item response theory for online testing, we can predict success/failure for term-end examinations for each student at early stages in courses. We applied the newly developed nearest neighbor method for determining the similarity of learning skills in the trends of estimated abilities, resulting in better prediction accuracy for success or failure. This paper shows that the use of the learning analytics incorporating trends for abilities is effective. ROC curve and recall precision curve are also utilized in the proposed method.

show abstract

Success/Failure Prediction for Final Examination Using the Trend of Weekly Online Testing

Hirose

2018

2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI)

View full text Add to dashboard Cite

Rediscovery of Initial Habituation Importance Learned from Analytics of Learning Check Testing in Mathematics for Undergraduate Students

Cited by 12 publications

References 9 publications

Key Factor Not to Drop Out is to Attend Lectures

Key Factor Not to Drop Out is to Attend Lectures

Prediction of Success or Failure for Examination using Nearest Neighbor Method to the Trend of Weekly Online Testing

Success/Failure Prediction for Final Examination Using the Trend of Weekly Online Testing

Contact Info

Product

Resources

About