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Following the recent developments in educational technology-integrated learning, interest in the true implementation of flipped and blended classrooms as innovative approaches has become increasingly popular among language education authorities. This research aimed at comparing flipped, blended, and traditional teaching (T-learning) contexts on Iranian EFL learners’ grammar learning. To this end, 60 intermediate EFL students out of 80, based on their performance in an Oxford Placement Test (OPT), were selected and divided into three groups, including two comparative and one control group, 20 in each. At the beginning of the study, the three groups participated in a pretest to assess their initial ability of grammar knowledge. To integrate technology into their instruction, both comparative groups received the same treatment and materials based on the Substitution, Augmentation, Modification, and Redefinition (SAMR) model. The blended comparative group received instruction in both on-line and T-learning contexts, while the flipped comparative group received instruction in an online context. The control group received instruction in a T-learning context. After the treatment sessions, they participated in a post-test. The findings showed that reading interesting English newspaper articles, both in the blended and flipped classrooms had a statistically significant effect on developing EFL learners' grammar knowledge. The findings of the study may be beneficial for EFL teachers and material developers to reconsider the role of educational technology (Ed Tech) tools to support classroom-based learning.

Today, news texts can be created automatically and presented to readers without human participation through technologies and methods such as big data, deep learning, and natural language generation. With this research, we have developed an application that can contribute to the literature regarding The Studies on Robot Journalism Applications with a technology-reductionist perspective. Robot journalism application named Robottan Al Haberi (the English equivalent of the application name is “get the news from the robot”) produces news text by placing weather, exchange rates, and earthquake data in certain templates. The news texts, which are produced by placing the data in appropriate spaces on the template and with a maximum length of 280 characters, are automatically shared via the Twitter account @robottanalhaber. The weather information is shared once a day, the exchange rate information is shared three times a day, and the earthquake information is shared instantly. Here, we aim to produce automatic and short news by using the available structured data by placing them in specific news templates suggesting different options or a combination of them for different situations.